data loading
data <- mtcars
data check
dim(data)
## [1] 32 11
dim(data)[1]
## [1] 32
dim(data)[2]
## [1] 11
basic summary
summary(data)
## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000
detail summary
library(psych)
## Warning: package 'psych' was built under R version 4.3.3
knitr::kable(describe(data))
| mpg |
1 |
32 |
20.090625 |
6.0269481 |
19.200 |
19.6961538 |
5.4114900 |
10.400 |
33.900 |
23.500 |
0.6106550 |
-0.3727660 |
1.0654240 |
| cyl |
2 |
32 |
6.187500 |
1.7859216 |
6.000 |
6.2307692 |
2.9652000 |
4.000 |
8.000 |
4.000 |
-0.1746119 |
-1.7621198 |
0.3157093 |
| disp |
3 |
32 |
230.721875 |
123.9386938 |
196.300 |
222.5230769 |
140.4763500 |
71.100 |
472.000 |
400.900 |
0.3816570 |
-1.2072119 |
21.9094727 |
| hp |
4 |
32 |
146.687500 |
68.5628685 |
123.000 |
141.1923077 |
77.0952000 |
52.000 |
335.000 |
283.000 |
0.7260237 |
-0.1355511 |
12.1203173 |
| drat |
5 |
32 |
3.596563 |
0.5346787 |
3.695 |
3.5792308 |
0.7042350 |
2.760 |
4.930 |
2.170 |
0.2659039 |
-0.7147006 |
0.0945187 |
| wt |
6 |
32 |
3.217250 |
0.9784574 |
3.325 |
3.1526923 |
0.7672455 |
1.513 |
5.424 |
3.911 |
0.4231465 |
-0.0227108 |
0.1729685 |
| qsec |
7 |
32 |
17.848750 |
1.7869432 |
17.710 |
17.8276923 |
1.4158830 |
14.500 |
22.900 |
8.400 |
0.3690453 |
0.3351142 |
0.3158899 |
| vs |
8 |
32 |
0.437500 |
0.5040161 |
0.000 |
0.4230769 |
0.0000000 |
0.000 |
1.000 |
1.000 |
0.2402577 |
-2.0019376 |
0.0890983 |
| am |
9 |
32 |
0.406250 |
0.4989909 |
0.000 |
0.3846154 |
0.0000000 |
0.000 |
1.000 |
1.000 |
0.3640159 |
-1.9247414 |
0.0882100 |
| gear |
10 |
32 |
3.687500 |
0.7378041 |
4.000 |
3.6153846 |
1.4826000 |
3.000 |
5.000 |
2.000 |
0.5288545 |
-1.0697507 |
0.1304266 |
| carb |
11 |
32 |
2.812500 |
1.6152000 |
2.000 |
2.6538462 |
1.4826000 |
1.000 |
8.000 |
7.000 |
1.0508738 |
1.2570431 |
0.2855297 |
visualization
boxplot(data$mpg)
boxplot(data$wt)
boxplot(data$mpg, data$wt)
hist(data$mpg)
hist(data$wt)
plot(data$wt, data$mpg)
Modeling
corr.test(data$wt, data$mpg)
## Call:corr.test(x = data$wt, y = data$mpg)
## Correlation matrix
## [1] -0.87
## Sample Size
## [1] 32
## These are the unadjusted probability values.
## The probability values adjusted for multiple tests are in the p.adj object.
## [1] 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option