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1 / 8

2 / 8

통계적 독립 데이터셋R 코드
파이썬 코드
library(tidyverse)
# Create first dataset
dataset1 <- data.frame(
  value = c(0.72, 0.61, 0.35, 0.19, 0.47, 0.99, 0.12, 0.84, 0.65, 0.38, 
            0.01, 0.25, 0.96, 0.77, 0.54, 0.82, 0.36, 0.43, 0.06, 0.92, 
            0.21, 0.17, 0.78, 0.68, 0.94, 0.40, 0.52, 0.29)
)
# Create second dataset
dataset2 <- data.frame(
  value = c(0.82, 0.23, 0.67, 0.42, 0.91, 0.17, 0.56, 0.95, 0.03, 0.87, 
            0.74, 0.45, 0.28, 0.88, 0.51, 0.85, 0.10, 0.72, 0.80, 0.35, 
            0.57, 0.90, 0.33, 0.06, 0.59, 0.01, 0.23, 0.39)
)
ind_df <- bind_cols(dataset1, dataset2) %>% 
  as_tibble() %>% 
  set_names(c("변수1", "변수2"))
# ind_df

ind_df

# A tibble: 28 × 2
   변수1 변수2
   <dbl> <dbl>
 1  0.72  0.82
 2  0.61  0.23
 3  0.35  0.67
 4  0.19  0.42
 5  0.47  0.91
 6  0.99  0.17
 7  0.12  0.56
 8  0.84  0.95
 9  0.65  0.03
10  0.38  0.87
# … with 18 more rows
# Create first dataset
dataset1 = {'value': [0.72, 0.61, 0.35, 0.19, 0.47, 0.99, 0.12, 0.84, 0.65, 0.38, 
                     0.01, 0.25, 0.96, 0.77, 0.54, 0.82, 0.36, 0.43, 0.06, 0.92, 
                     0.21, 0.17, 0.78, 0.68, 0.94, 0.40, 0.52, 0.29]}
# Create second dataset
dataset2 = {'value': [0.82, 0.23, 0.67, 0.42, 0.91, 0.17, 0.56, 0.95, 0.03, 0.87, 
                     0.74, 0.45, 0.28, 0.88, 0.51, 0.85, 0.10, 0.72, 0.80, 0.35, 
                     0.57, 0.90, 0.33, 0.06, 0.59, 0.01, 0.23, 0.39, 0.64]}
# View first few rows of first dataset
print(list(dataset1.values())[:5])
# [0.72, 0.61, 0.35, 0.19, 0.47]
3 / 8

통계적으로 독립 : 연속형 두 변수## 데이터셋
ind_df %>% slice_head(n=5)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91
3 / 8

통계적으로 독립 : 연속형 두 변수

## 데이터셋
ind_df %>% slice_head(n=5)
#
#
#
## 시각화
ggplot(data = ind_df)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91

3 / 8

통계적으로 독립 : 연속형 두 변수

## 데이터셋
ind_df %>% slice_head(n=5)
#
#
#
## 시각화
ggplot(data = ind_df) +
  aes(x = 변수1)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91

3 / 8

통계적으로 독립 : 연속형 두 변수

## 데이터셋
ind_df %>% slice_head(n=5)
#
#
#
## 시각화
ggplot(data = ind_df) +
  aes(x = 변수1) +
  aes(y = 변수2)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91

3 / 8

통계적으로 독립 : 연속형 두 변수

## 데이터셋
ind_df %>% slice_head(n=5)
#
#
#
## 시각화
ggplot(data = ind_df) +
  aes(x = 변수1) +
  aes(y = 변수2) +
  geom_point(color = "steelblue",
             alpha = .8)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91

3 / 8

통계적으로 독립 : 연속형 두 변수

## 데이터셋
ind_df %>% slice_head(n=5)
#
#
#
## 시각화
ggplot(data = ind_df) +
  aes(x = 변수1) +
  aes(y = 변수2) +
  geom_point(color = "steelblue",
             alpha = .8)
#
#
#
#
#
#
## 통계검정
cor.test(x = ind_df$변수1,
         y = ind_df$변수1)

# A tibble: 5 × 2
  변수1 변수2
  <dbl> <dbl>
1  0.72  0.82
2  0.61  0.23
3  0.35  0.67
4  0.19  0.42
5  0.47  0.91


    Pearson's product-moment correlation
data:  ind_df$변수1 and ind_df$변수1
t = Inf, df = 26, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 1 1
sample estimates:
cor 
  1

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Y (연속형), X (연속형)4 / 8

통계적으로 독립 : 연속형 두 변수5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.126       -0.817
2       0.838       -2.05 
3      -0.517        0.247


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -1.7116, df = 48, p-value = 0.09343
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.48575406  0.04126876
sample estimates:
       cor 
-0.2398339

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -0.992        0.456
2       0.519       -0.982
3       0.544        1.00


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.68328, df = 48, p-value = 0.4977
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3664821  0.1852626
sample estimates:
        cor 
-0.09814626

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.780       -0.927
2      -0.153       -0.618
3       1.01        -0.690


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -1.083, df = 48, p-value = 0.2842
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4149515  0.1294715
sample estimates:
      cor 
-0.154442

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.760      0.00493
2       0.435      1.46   
3       0.799     -1.06


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -1.7686, df = 48, p-value = 0.08332
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.49182941  0.03329733
sample estimates:
       cor 
-0.2473428

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       2.54        -2.16 
2       1.94        -0.502
3       0.344       -0.577


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.91172, df = 48, p-value = 0.3665
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1534495  0.3944922
sample estimates:
      cor 
0.1304709

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -1.17        -0.204
2      -0.361       -0.448
3      -1.13        -0.756


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 1.0593, df = 48, p-value = 0.2948
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1327916  0.4121511
sample estimates:
      cor 
0.1511427

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -0.898       -0.875
2       0.703       -0.926
3       0.775       -1.69


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.22933, df = 48, p-value = 0.8196
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3085887  0.2475446
sample estimates:
        cor 
-0.03308263

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1     -0.966        0.350 
2     -0.0761       0.980 
3     -0.547       -0.0916


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -1.1484, df = 48, p-value = 0.2565
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4226367  0.1202971
sample estimates:
       cor 
-0.1635262

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -0.689        2.17 
2      -1.53         0.675
3      -0.193        1.22


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.98493, df = 48, p-value = 0.3296
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1432103  0.4032958
sample estimates:
      cor 
0.1407479

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.441        0.453
2      -0.283       -0.655
3      -0.372        0.203


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.26905, df = 48, p-value = 0.789
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2421581  0.3137639
sample estimates:
       cor 
0.03880529

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      0.289         1.25 
2      0.0924        1.49 
3      1.41          0.669


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.20304, df = 48, p-value = 0.84
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3051537  0.2511010
sample estimates:
        cor 
-0.02929417

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.859       -0.719
2      -1.04        -0.569
3       0.826        0.644


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.18744, df = 48, p-value = 0.8521
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2532097  0.3031101
sample estimates:
       cor 
0.02704411

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -0.906       -1.47 
2      -1.44        -0.600
3       0.820        0.736


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 1.2368, df = 48, p-value = 0.2222
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1078909  0.4329063
sample estimates:
      cor 
0.1757343

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       1.38         0.114
2       0.594       -1.77 
3       1.83         0.784


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.31639, df = 48, p-value = 0.7531
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3199051  0.2357213
sample estimates:
        cor 
-0.04561959

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      -0.257        0.254
2       2.11         0.197
3       1.51        -0.105


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.36502, df = 48, p-value = 0.7167
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3261838  0.2290897
sample estimates:
        cor 
-0.05261299

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.354        0.842
2      -0.134        0.329
3       0.787        0.172


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.48954, df = 48, p-value = 0.6267
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2120241  0.3421190
sample estimates:
       cor 
0.07048327

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.623        0.823
2       0.380        0.719
3      -0.197       -1.63


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.44066, df = 48, p-value = 0.6614
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2187364  0.3358892
sample estimates:
       cor 
0.06347607

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       2.81         1.36 
2      -0.632        2.27 
3       0.223        0.496


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.86615, df = 48, p-value = 0.3907
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3889695  0.1598132
sample estimates:
       cor 
-0.1240528

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1      0.0784       -0.519
2      0.816        -1.74 
3     -0.605         1.66


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = -0.038981, df = 48, p-value = 0.9691
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2835300  0.2731491
sample estimates:
        cor 
-0.00562635

5 / 8

두변수(머리털 길이와 뇌 무게) 통계적 독립

library(tidyverse)
tibble(hair_length = rnorm(n = 50),
       brain_weight = rnorm(n = 50)) ->
ind_data; ind_data %>% slice_head( n = 3)
ggplot(data = ind_data) +
  aes(x = hair_length) +
  aes(y = brain_weight) +
  geom_point(color = "steelblue",
             alpha = .8) +
  theme(panel.background =
          element_rect(
            color =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, "red", "black"),
            size =
              ifelse(cor.test(ind_data$hair_length, ind_data$brain_weight)[[3]]<.05, 8, 3))) +
  labs(x = "머리털 길이", y = "뇌 무게")
cor.test(x = ind_data$hair_length,
         y = ind_data$brain_weight)

# A tibble: 3 × 2
  hair_length brain_weight
        <dbl>        <dbl>
1       0.401        0.562
2       0.202        1.74 
3      -2.04         0.889


    Pearson's product-moment correlation
data:  ind_data$hair_length and ind_data$brain_weight
t = 0.54774, df = 48, p-value = 0.5864
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2040093  0.3494946
sample estimates:
       cor 
0.07881394

5 / 8

Y (연속형), X (2 범주)6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    167.
2 여성    161.
3 여성    183.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.85102, df = 45.834, p-value = 0.3992
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -8.855583  3.593026
sample estimates:
mean in group 남성 mean in group 여성 
          168.3132           170.9445

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    177.
2 남성    172.
3 여성    174.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.3146, df = 42.926, p-value = 0.7546
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -6.444135  4.704963
sample estimates:
mean in group 남성 mean in group 여성 
          169.2713           170.1408

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    187.
2 여성    188.
3 여성    182.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -1.4062, df = 37.611, p-value = 0.1679
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -11.18836   2.01795
sample estimates:
mean in group 남성 mean in group 여성 
          168.7906           173.3758

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    172.
2 남성    172.
3 여성    190.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -2.5724, df = 47.992, p-value = 0.01325
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -14.464167  -1.772919
sample estimates:
mean in group 남성 mean in group 여성 
          166.6604           174.7789

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    170.
2 여성    172.
3 남성    169.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.41433, df = 47.411, p-value = 0.6805
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -7.128098  4.692925
sample estimates:
mean in group 남성 mean in group 여성 
          169.9255           171.1431

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    191.
2 여성    176.
3 여성    184.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.56102, df = 42.725, p-value = 0.5777
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -8.507627  4.804919
sample estimates:
mean in group 남성 mean in group 여성 
          168.3479           170.1993

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    172.
2 남성    174.
3 남성    179.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -1.1719, df = 46.689, p-value = 0.2472
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -9.231282  2.435958
sample estimates:
mean in group 남성 mean in group 여성 
          171.0713           174.4690

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    155.
2 남성    163.
3 여성    173.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 0.12071, df = 45.568, p-value = 0.9045
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -5.033867  5.675951
sample estimates:
mean in group 남성 mean in group 여성 
          168.9828           168.6618

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    160.
2 여성    151.
3 남성    159.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.58033, df = 46.956, p-value = 0.5645
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -6.934225  3.829343
sample estimates:
mean in group 남성 mean in group 여성 
          168.2185           169.7710

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    178.
2 남성    175.
3 여성    176.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -1.6925, df = 41.7, p-value = 0.09802
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -8.2292225  0.7230509
sample estimates:
mean in group 남성 mean in group 여성 
          166.4234           170.1765

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    169.
2 남성    184.
3 남성    170.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 0.82257, df = 47.402, p-value = 0.4149
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -2.931505  6.988623
sample estimates:
mean in group 남성 mean in group 여성 
          174.1101           172.0815

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    147.
2 남성    140.
3 여성    154.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.63894, df = 45.309, p-value = 0.5261
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -9.201881  4.769010
sample estimates:
mean in group 남성 mean in group 여성 
          167.5944           169.8109

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    164.
2 남성    160.
3 남성    164.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 1.5526, df = 47.974, p-value = 0.1271
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -1.225737  9.534091
sample estimates:
mean in group 남성 mean in group 여성 
          172.1478           167.9936

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    172.
2 남성    169.
3 여성    160.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 1.4574, df = 44.749, p-value = 0.152
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -1.511812  9.423371
sample estimates:
mean in group 남성 mean in group 여성 
          168.9510           164.9952

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    159.
2 남성    175.
3 여성    175.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 1.7418, df = 47.964, p-value = 0.08795
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -0.6722814  9.3823483
sample estimates:
mean in group 남성 mean in group 여성 
          171.2503           166.8953

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    145.
2 남성    166.
3 여성    170.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.6264, df = 47.952, p-value = 0.534
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -6.860411  3.601249
sample estimates:
mean in group 남성 mean in group 여성 
          167.1057           168.7353

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    167.
2 여성    174.
3 남성    186.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 0.79931, df = 45.599, p-value = 0.4283
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -3.016733  6.988982
sample estimates:
mean in group 남성 mean in group 여성 
          170.3059           168.3197

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    160.
2 여성    164.
3 여성    177.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.79109, df = 40.857, p-value = 0.4335
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -7.051308  3.082229
sample estimates:
mean in group 남성 mean in group 여성 
          169.0426           171.0271

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 남성    168.
2 여성    154.
3 남성    183.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = 0.45348, df = 25.427, p-value = 0.654
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -5.290816  8.281870
sample estimates:
mean in group 남성 mean in group 여성 
          167.7157           166.2202

6 / 8

두집단(남/녀) 신장이 독립

tibble(sex = sample(x = c("남성","여성"),
                    size = 50,
                    replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
  height_data; height_data %>% slice_head( n = 3)
#
#
#
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = sex) +
  aes(y = height) +
  aes(group = sex) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs(x= "성별", y = "신장")
# statistical test
t.test(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
         height_data$sex)  # the discrete variable

# A tibble: 3 × 2
  sex   height
  <chr>  <dbl>
1 여성    170.
2 여성    170.
3 남성    183.


    Welch Two Sample t-test
data:  height_data$height by height_data$sex
t = -0.1438, df = 46.298, p-value = 0.8863
alternative hypothesis: true difference in means between group 남성 and group 여성 is not equal to 0
95 percent confidence interval:
 -5.773838  5.003770
sample estimates:
mean in group 남성 mean in group 여성 
          172.4989           172.8839

6 / 8

Y (연속형), X (N 범주)7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     173.
2 3반     170.
3 2반     205.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                   diff        lwr       upr     p adj
2반-1반    -0.507663765 -12.875113 11.859785 0.9995242
3반-1반    -2.954968492 -13.171057  7.261120 0.8671120
특수반-1반 -0.505066084 -11.352036 10.341904 0.9993065
3반-2반    -2.447304727 -14.064500  9.169891 0.9429070
특수반-2반  0.002597681 -12.173096 12.178292 1.0000000
특수반-3반  2.449902408  -7.533193 12.432998 0.9135634

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     172.
2 3반     172.
3 1반     162.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff       lwr       upr     p adj
2반-1반     0.9415869 -11.19792 13.081099 0.9968290
3반-1반    -1.0328743 -12.98240 10.916649 0.9956295
특수반-1반 -3.0043967 -15.14391  9.135115 0.9115892
3반-2반    -1.9744613 -13.09063  9.141709 0.9645455
특수반-2반 -3.9459837 -15.26614  7.374170 0.7894170
특수반-3반 -1.9715224 -13.08769  9.144647 0.9646944

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     167.
2 3반     171.
3 2반     167.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반    -1.5178444 -11.721244  8.685556 0.9786196
3반-1반    -0.7390442  -9.575448  8.097359 0.9960354
특수반-1반 -4.3012202 -13.907404  5.304963 0.6340056
3반-2반     0.7788002  -9.424600 10.982200 0.9969766
특수반-2반 -2.7833758 -13.660237  8.093485 0.9033799
특수반-3반 -3.5621760 -13.168360  6.044008 0.7567074

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 2반      160.
2 1반      166.
3 특수반   155.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff       lwr      upr     p adj
2반-1반    -0.8917641 -13.07714 11.29361 0.9973315
3반-1반     0.1908132 -12.28924 12.67087 0.9999753
특수반-1반 -1.6278026 -13.55815 10.30254 0.9833398
3반-2반     1.0825773 -11.84699 14.01214 0.9960224
특수반-2반 -0.7360386 -13.13584 11.66376 0.9985687
특수반-3반 -1.8186159 -14.50812 10.87088 0.9807962

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     171.
2 1반     177.
3 3반     157.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr        upr     p adj
2반-1반    -7.029198 -17.683519  3.6251236 0.3061771
3반-1반    -8.690892 -17.745324  0.3635402 0.0641399
특수반-1반 -2.326404 -11.380836  6.7280286 0.9023413
3반-2반    -1.661694 -11.387714  8.0643263 0.9682400
특수반-2반  4.702794  -5.023226 14.4288146 0.5745900
특수반-3반  6.364488  -1.576774 14.3057506 0.1570043

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 2반     183.
2 3반     186.
3 1반     163.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반     6.0228745  -5.284201 17.329950 0.4937312
3반-1반     4.9100396  -4.945968 14.766048 0.5502779
특수반-1반  6.9684918  -4.699264 18.636247 0.3931763
3반-2반    -1.1128349 -11.996273  9.770603 0.9928257
특수반-2반  0.9456173 -11.602085 13.493320 0.9970887
특수반-3반  2.0584522  -9.199247 13.316151 0.9615234

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 특수반   173.
2 3반      171.
3 1반      160.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반     0.1944076 -10.008963 10.397778 0.9999522
3반-1반    -3.0937827 -12.288317  6.100751 0.8064722
특수반-1반  3.5734003  -5.276646 12.423447 0.7055770
3반-2반    -3.2881903 -12.776018  6.199637 0.7922900
특수반-2반  3.3789927  -5.775390 12.533376 0.7592784
특수반-3반  6.6671831  -1.347392 14.681758 0.1337197

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 1반      178.
2 2반      181.
3 특수반   157.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반    -0.6269522 -12.068942 10.815038 0.9988725
3반-1반    -5.9847571 -17.838865  5.869351 0.5392161
특수반-1반 -2.4449258 -14.078938  9.189086 0.9432865
3반-2반    -5.3578049 -16.529625  5.814016 0.5811496
특수반-2반 -1.8179736 -12.755978  9.120031 0.9706323
특수반-3반  3.5398313  -7.828576 14.908238 0.8399599

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 3반      170.
2 1반      159.
3 특수반   151.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr       upr     p adj
2반-1반     7.362096  -4.288616 19.012807 0.3434756
3반-1반     5.310581  -6.930985 17.552148 0.6569685
특수반-1반  1.685360  -9.342887 12.713606 0.9768984
3반-2반    -2.051514 -14.512875 10.409846 0.9714190
특수반-2반 -5.676736 -16.948461  5.594989 0.5412664
특수반-3반 -3.625222 -15.506663  8.256219 0.8478744

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 1반      162.
2 특수반   169.
3 특수반   159.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr      upr     p adj
2반-1반     4.3819317  -6.511688 15.27555 0.7080003
3반-1반     3.6282720  -7.265348 14.52189 0.8111850
특수반-1반  4.2143404  -6.679279 15.10796 0.7321629
3반-2반    -0.7536597 -12.058503 10.55118 0.9979775
특수반-2반 -0.1675913 -11.472435 11.13725 0.9999775
특수반-3반  0.5860684 -10.718775 11.89091 0.9990442

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 2반      182.
2 3반      196.
3 특수반   185.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반     4.9569108  -6.345439 16.259260 0.6491523
3반-1반     8.4427600  -3.806325 20.691845 0.2694392
특수반-1반  0.6507311 -11.032703 12.334165 0.9988162
3반-2반     3.4858492  -6.714767 13.686465 0.7991426
특수반-2반 -4.3061796 -13.820123  5.207763 0.6259570
특수반-3반 -7.7920288 -18.413332  2.829275 0.2198131

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 2반     167.
2 3반     169.
3 1반     165.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff       lwr       upr     p adj
2반-1반    -1.2784275 -9.098871  6.542016 0.9719880
3반-1반    -0.9964659 -9.306151  7.313219 0.9885548
특수반-1반  0.7856471 -9.075129 10.646423 0.9965663
3반-2반     0.2819616 -8.145597  8.709521 0.9997419
특수반-2반  2.0640746 -7.896236 12.024385 0.9454379
특수반-3반  1.7821130 -8.566767 12.130992 0.9675213

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 특수반   176.
2 특수반   183.
3 특수반   182.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr       upr     p adj
2반-1반     2.608015  -8.460510 13.676540 0.9225075
3반-1반     3.440079  -8.168688 15.048846 0.8586704
특수반-1반  1.416289  -8.937368 11.769946 0.9832161
3반-2반     0.832064 -10.776703 12.440831 0.9974915
특수반-2반 -1.191726 -11.545383  9.161931 0.9898464
특수반-3반 -2.023790 -12.953082  8.905503 0.9601327

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     184.
2 3반     159.
3 2반     173.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr       upr     p adj
2반-1반    -8.611609 -20.191754  2.968535 0.2095387
3반-1반    -3.545602 -15.713022  8.621819 0.8645720
특수반-1반 -2.492975 -13.454424  8.468474 0.9295892
3반-2반     5.066008  -7.319876 17.451891 0.6972066
특수반-2반  6.118634  -5.084819 17.322087 0.4720622
특수반-3반  1.052627 -10.756850 12.862103 0.9952138

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 1반      181.
2 특수반   155.
3 3반      148.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr      upr     p adj
2반-1반    -0.6167487 -14.213979 12.98048 0.9993587
3반-1반    -3.1711394 -16.382554 10.04027 0.9185444
특수반-1반  1.2636609 -11.331134 13.85846 0.9932146
3반-2반    -2.5543907 -17.292340 12.18356 0.9669205
특수반-2반  1.8804096 -12.307421 16.06824 0.9846849
특수반-3반  4.4348003  -9.383714 18.25332 0.8275308

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 특수반   181.
2 3반      174.
3 특수반   170.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반    -1.7022876 -12.013301  8.608726 0.9711888
3반-1반    -0.6117371  -9.147579  7.924105 0.9974924
특수반-1반  1.5169569  -6.708386  9.742300 0.9605828
3반-2반     1.0905505  -9.469816 11.650917 0.9926137
특수반-2반  3.2192446  -7.091769 13.530258 0.8388842
특수반-3반  2.1286941  -6.407148 10.664536 0.9097742

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 3반     187.
2 1반     156.
3 2반     183.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr       upr     p adj
2반-1반    -1.710227 -12.856377  9.435924 0.9766309
3반-1반     2.187413  -7.946802 12.321628 0.9389604
특수반-1반  0.836745 -12.216290 13.889780 0.9982003
3반-2반     3.897639  -6.478862 14.274141 0.7494392
특수반-2반  2.546972 -10.695052 15.788995 0.9556536
특수반-3반 -1.350668 -13.752959 11.051624 0.9913661

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     153.
2 3반     161.
3 2반     161.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                diff        lwr       upr     p adj
2반-1반     2.334310 -10.908206 15.576826 0.9653001
3반-1반     4.332345  -9.044616 17.709306 0.8236343
특수반-1반  2.176488 -11.066028 15.419004 0.9715543
3반-2반     1.998035  -8.189556 12.185625 0.9531745
특수반-2반 -0.157822 -10.168223  9.852579 0.9999729
특수반-3반 -2.155857 -12.343447  8.031734 0.9421920

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class height
  <chr>  <dbl>
1 1반     163.
2 1반     166.
3 3반     174.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr       upr     p adj
2반-1반    -2.2323418 -11.552461  7.087777 0.9190102
3반-1반     7.0739378  -3.165037 17.312913 0.2674992
특수반-1반 -0.9556717 -10.911486  9.000143 0.9940434
3반-2반     9.3062796  -1.077936 19.690495 0.0935213
특수반-2반  1.2766701  -8.828456 11.381796 0.9866748
특수반-3반 -8.0296095 -18.987965  2.928746 0.2207354

7 / 8

N 학급 신장이 독립

tibble(class = sample(x = c("1반","2반","3반","특수반"),
                  size = 50,
                  replace = TRUE)) %>%
  mutate(height = rnorm(n = 50, mean = 170, sd = 10)) ->
height_data; height_data %>% slice_head( n = 3)
#
#
#
#
# visualization
ggplot(height_data) +
  aes(x = class) +
  aes(y = height) +
  aes(group = class) +
  geom_boxplot() +
  geom_jitter(height = 0,
              width = .02) +
  stat_summary(fun.y = mean,
               geom = "point",
               col = "goldenrod3",
               size = 5) +
  labs( x = "학년", y = "신장")
# statistical test
TukeyHSD(aov(height_data$height ~ # the continuous variable by (~)  # the continuous variable by (~)
             height_data$class))  # the discrete variable

# A tibble: 3 × 2
  class  height
  <chr>   <dbl>
1 1반      164.
2 특수반   166.
3 2반      154.

  Tukey multiple comparisons of means
    95% family-wise confidence level
Fit: aov(formula = height_data$height ~ height_data$class)
$`height_data$class`
                 diff        lwr      upr     p adj
2반-1반    -1.4028727 -13.520144 10.71440 0.9896719
3반-1반    -0.3325582 -12.088038 11.42292 0.9998439
특수반-1반  0.1860896 -12.263223 12.63540 0.9999769
3반-2반     1.0703145  -8.376831 10.51746 0.9903059
특수반-2반  1.5889623  -8.708738 11.88666 0.9762505
특수반-3반  0.5186478  -9.350784 10.38808 0.9990047

7 / 8

Y (범주), X (범주)8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  오른손잡이
2 남자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         40       14
  여자         32       14


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.076761, df = 1, p-value = 0.7817

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 남자  오른손잡이
3 남자  왼손잡이

      y
x      오른손잡이 왼손잡이
  남자         31        8
  여자         42       19


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.87884, df = 1, p-value = 0.3485

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  왼손잡이  
2 여자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         29       24
  여자         34       13


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 2.6061, df = 1, p-value = 0.1065

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 남자  오른손잡이
3 여자  왼손잡이

      y
x      오른손잡이 왼손잡이
  남자         38       14
  여자         29       19


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 1.2821, df = 1, p-value = 0.2575

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  왼손잡이  
2 남자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         34       20
  여자         38        8


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 3.8309, df = 1, p-value = 0.05031

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 남자  오른손잡이
3 남자  왼손잡이

      y
x      오른손잡이 왼손잡이
  남자         37       16
  여자         32       15


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0, df = 1, p-value = 1

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 여자  왼손잡이  
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         37       14
  여자         33       16


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.12195, df = 1, p-value = 0.7269

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 여자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         35       15
  여자         37       13


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.049603, df = 1, p-value = 0.8238

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 여자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         35       15
  여자         34       16


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0, df = 1, p-value = 1

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  왼손잡이  
2 여자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         38       11
  여자         44        7


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.76518, df = 1, p-value = 0.3817

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 여자  왼손잡이  
3 남자  왼손잡이

      y
x      오른손잡이 왼손잡이
  남자         39       14
  여자         33       14


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.023019, df = 1, p-value = 0.8794

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 남자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         39       13
  여자         31       17


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.84135, df = 1, p-value = 0.359

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  왼손잡이  
2 여자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         38       14
  여자         32       16


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.23085, df = 1, p-value = 0.6309

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  오른손잡이
2 남자  왼손잡이  
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         32       16
  여자         33       19


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.01585, df = 1, p-value = 0.8998

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  오른손잡이
2 남자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         41       12
  여자         34       13


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.12043, df = 1, p-value = 0.7286

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 남자  왼손잡이  
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         32       20
  여자         34       14


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.59139, df = 1, p-value = 0.4419

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  오른손잡이
2 남자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         39       14
  여자         35       12


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 4.8721e-31, df = 1, p-value = 1

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  왼손잡이  
2 남자  오른손잡이
3 여자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         32       16
  여자         37       15


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.071999, df = 1, p-value = 0.7884

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 남자  왼손잡이  
2 남자  오른손잡이
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         36       12
  여자         43        9


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.48695, df = 1, p-value = 0.4853

8 / 8

# 데이터
tibble(sex = sample(x = c("남자","여자"),
                  size = 100,
                  replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"),
                  size = 100,
                  replace = TRUE,
                  prob = c(.3,.7))) ->
handed_data ; handed_data %>% slice_head( n = 3)
# 시각화
library(ggmosaic); library(janitor); library(patchwork)
ggplot(data = handed_data) +
  geom_mosaic(aes(x = product(sex), fill = handed)) +
  labs(x = "성별", y = "손잡이",
       fill = "손잡이 구분")
# 표
table(x = handed_data$sex, y = handed_data$handed)
#
#
#
#
# 검정통계량
chisq.test(x = handed_data$sex,
           y = handed_data$handed)

# A tibble: 3 × 2
  sex   handed    
  <chr> <chr>     
1 여자  오른손잡이
2 여자  왼손잡이  
3 남자  오른손잡이

      y
x      오른손잡이 왼손잡이
  남자         33       19
  여자         32       16


    Pearson's Chi-squared test with Yates' continuity correction
data:  handed_data$sex and handed_data$handed
X-squared = 0.01585, df = 1, p-value = 0.8998

8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)

8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))

8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data

# A tibble: 100 × 2
   sex   handed    
   <chr> <chr>     
 1 여자  왼손잡이  
 2 남자  오른손잡이
 3 남자  오른손잡이
 4 남자  오른손잡이
 5 남자  오른손잡이
 6 여자  왼손잡이  
 7 남자  오른손잡이
 8 남자  오른손잡이
 9 여자  오른손잡이
10 남자  오른손잡이
# … with 90 more rows
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed)

  sex 오른손잡이 왼손잡이
 남자         33       11
 여자         36       20
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계")

  sex 오른손잡이 왼손잡이 합계
 남자         45       11   56
 여자         28       16   44
 합계         73       27  100
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col")

  sex 오른손잡이  왼손잡이 합계
 남자  0.5822785 0.3809524 0.54
 여자  0.4177215 0.6190476 0.46
 합계  1.0000000 1.0000000 1.00
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0)

  sex 오른손잡이 왼손잡이 합계
 남자        42%      66%  49%
 여자        58%      34%  51%
 합계       100%     100% 100%
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear")

  sex 오른손잡이  왼손잡이       합계
 남자   60% (40)  52% (17)  57%  (57)
 여자   40% (27)  48% (16)  43%  (43)
 합계  100% (67) 100% (33) 100% (100)
8 / 8

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt()





  
      sex
      오른손잡이
      왼손잡이
      합계
    

  남자
44% (31)
55% (16)
47%  (47)
여자
56% (40)
45% (13)
53%  (53)
합계
100% (71)
100% (29)
100% (100)




8 / 8

sex	오른손잡이	왼손잡이	합계
남자	44% (31)	55% (16)	47% (47)
여자	56% (40)	45% (13)	53% (53)
합계	100% (71)	100% (29)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    )




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      오른손잡이
      왼손잡이
      합계
    

  남자
42% (30)
38% (11)
41%  (41)
여자
58% (41)
62% (18)
59%  (59)
합계
100% (71)
100% (29)
100% (100)




8 / 8

☛ 2변량 교차 범주형 데이터 ☚
남자	42% (30)	38% (11)	41% (41)
여자	58% (41)	62% (18)	59% (59)
합계	100% (71)	100% (29)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    ) %>%
    cols_align(
      align = "right",
      columns = c(오른손잡이, 왼손잡이, 합계)
    )




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      오른손잡이
      왼손잡이
      합계
    

  남자
49% (33)
66% (21)
54%  (54)
여자
51% (35)
34% (11)
46%  (46)
합계
100% (68)
100% (32)
100% (100)




8 / 8

☛ 2변량 교차 범주형 데이터 ☚
남자	49% (33)	66% (21)	54% (54)
여자	51% (35)	34% (11)	46% (46)
합계	100% (68)	100% (32)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    ) %>%
    cols_align(
      align = "right",
      columns = c(오른손잡이, 왼손잡이, 합계)
    ) %>%
    tab_options(
      table.width = pct(70),
      heading.background.color = "#1E61B0",  # R logo 파란색
      heading.title.font.size = "17px",
      column_labels.background.color = "#F7F7F7",  # R logo 회색
      column_labels.font.weight = "bold",
      stub.background.color = "#ffffff",
      stub.font.weight = "bold"
    )




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      오른손잡이
      왼손잡이
      합계
    

  남자
39% (25)
47% (17)
42%  (42)
여자
61% (39)
53% (19)
58%  (58)
합계
100% (64)
100% (36)
100% (100)




8 / 8

☛ 2변량 교차 범주형 데이터 ☚
남자	39% (25)	47% (17)	42% (42)
여자	61% (39)	53% (19)	58% (58)
합계	100% (64)	100% (36)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    ) %>%
    cols_align(
      align = "right",
      columns = c(오른손잡이, 왼손잡이, 합계)
    ) %>%
    tab_options(
      table.width = pct(70),
      heading.background.color = "#1E61B0",  # R logo 파란색
      heading.title.font.size = "17px",
      column_labels.background.color = "#F7F7F7",  # R logo 회색
      column_labels.font.weight = "bold",
      stub.background.color = "#ffffff",
      stub.font.weight = "bold"
    )  %>%
  tab_spanner(label = "요약 통계량", columns =  c(오른손잡이, 왼손잡이))




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      
        요약 통계량
      
      합계
    

      오른손잡이
      왼손잡이
    

  남자
46% (30)
57% (20)
50%  (50)
여자
54% (35)
43% (15)
50%  (50)
합계
100% (65)
100% (35)
100% (100)




8 / 8

☛ 2변량 교차 범주형 데이터 ☚
남자	46% (30)	57% (20)	50% (50)
여자	54% (35)	43% (15)	50% (50)
합계	100% (65)	100% (35)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    ) %>%
    cols_align(
      align = "right",
      columns = c(오른손잡이, 왼손잡이, 합계)
    ) %>%
    tab_options(
      table.width = pct(70),
      heading.background.color = "#1E61B0",  # R logo 파란색
      heading.title.font.size = "17px",
      column_labels.background.color = "#F7F7F7",  # R logo 회색
      column_labels.font.weight = "bold",
      stub.background.color = "#ffffff",
      stub.font.weight = "bold"
    )  %>%
  tab_spanner(label = "요약 통계량", columns =  c(오른손잡이, 왼손잡이)) %>%
  tab_style(
    style = list(
      cell_fill(color = "lightblue"),
      cell_text(
        align = "right",
        size = "large",
        weight = "bold")
      ),
    locations = cells_body(rows = sex == "합계",
                           columns = 합계))




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      
        요약 통계량
      
      합계
    

      오른손잡이
      왼손잡이
    

  남자
57% (46)
68% (13)
59%  (59)
여자
43% (35)
32%  (6)
41%  (41)
합계
100% (81)
100% (19)
100% (100)




8 / 8

☛ 2변량 교차 범주형 데이터 ☚
남자	57% (46)	68% (13)	59% (59)
여자	43% (35)	32% (6)	41% (41)
합계	100% (81)	100% (19)	100% (100)

데이터 → 요약통계 → 표library(gt); library(janitor)
handed_data <- tibble(sex = sample(x = c("남자","여자"), size = 100, replace = TRUE),
       handed = sample(x = c("왼손잡이", "오른손잡이"), size = 100, replace = T, prob = c(.3,.7)))
# 표
handed_data %>%
  tabyl(sex, handed) %>%
  adorn_totals(where = c("row", "col"), name = "합계") %>%
  adorn_percentages(denominator = "col") %>%
  adorn_pct_formatting(digits = 0) %>%
  adorn_ns(position = "rear") %>%
# 표 외양
  gt() %>%
    tab_header(
      title = md("**&#9755; 2변량 교차 범주형 데이터 &#9754;**"),
    ) %>%
    cols_align(
      align = "right",
      columns = c(오른손잡이, 왼손잡이, 합계)
    ) %>%
    tab_options(
      table.width = pct(70),
      heading.background.color = "#1E61B0",  # R logo 파란색
      heading.title.font.size = "17px",
      column_labels.background.color = "#F7F7F7",  # R logo 회색
      column_labels.font.weight = "bold",
      stub.background.color = "#ffffff",
      stub.font.weight = "bold"
    )  %>%
  tab_spanner(label = "요약 통계량", columns =  c(오른손잡이, 왼손잡이)) %>%
  tab_style(
    style = list(
      cell_fill(color = "lightblue"),
      cell_text(
        align = "right",
        size = "large",
        weight = "bold")
      ),
    locations = cells_body(rows = sex == "합계",
                           columns = 합계))




  
      ☛ 2변량 교차 범주형 데이터 ☚
    

  
      sex
      
        요약 통계량
      
      합계
    

      오른손잡이
      왼손잡이
    

  남자
49% (33)
58% (19)
52%  (52)
여자
51% (34)
42% (14)
48%  (48)
합계
100% (67)
100% (33)
100% (100)




8 / 8

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