ABTest(Personal Project)

Project ABTest

ABTest

• ABTest 실습문제 • datasets 패키지에 있는 mtcars라는 데이터셋을 메모리로 로딩하시오.

data("mtcars")
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(psych)
headTail(mtcars)#내용조회

##                 mpg cyl disp  hp drat   wt  qsec  vs  am gear carb
## Mazda RX4        21   6  160 110  3.9 2.62 16.46   0   1    4    4
## Mazda RX4 Wag    21   6  160 110  3.9 2.88 17.02   0   1    4    4
## Datsun 710     22.8   4  108  93 3.85 2.32 18.61   1   1    4    1
## Hornet 4 Drive 21.4   6  258 110 3.08 3.21 19.44   1   0    3    1
## ...             ... ...  ... ...  ...  ...   ... ... ...  ...  ...
## Ford Pantera L 15.8   8  351 264 4.22 3.17  14.5   0   1    5    4
## Ferrari Dino   19.7   6  145 175 3.62 2.77  15.5   0   1    5    6
## Maserati Bora    15   8  301 335 3.54 3.57  14.6   0   1    5    8
## Volvo 142E     21.4   4  121 109 4.11 2.78  18.6   1   1    4    2

glimpse(mtcars)#구조파악

## Observations: 32
## Variables: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19....
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, ...
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 1...
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, ...
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.9...
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3...
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 2...
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, ...
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, ...
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, ...

summary(mtcars)#기술통게 분석

##       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

•변속기유형(am)에 따른 연비(mpg) 차이에 대해 요약집계 분석을 수행하시오. •- 변속기유형(am) –> 0: 자동기어, 1: 수동기어 •- 연비(mpg) –> 1갤런당 주행마일

t<-tapply(mtcars$mpg, list(mtcars$am), mean, na.rm=T)

•변속기유형(am)에 따른 연비(mpg) 차이에 대한 가설검정을 실시하시오.

1. 성과변수 정규성 검정

shapiro.test(mtcars$mpg[mtcars$am==1])

## 
##  Shapiro-Wilk normality test
## 
## data:  mtcars$mpg[mtcars$am == 1]
## W = 0.9458, p-value = 0.5363

shapiro.test(mtcars$mpg[mtcars$am==0])

## 
##  Shapiro-Wilk normality test
## 
## data:  mtcars$mpg[mtcars$am == 0]
## W = 0.97677, p-value = 0.8987

2. 성과변수 분산의 동질성 검정

mpg_1<-mtcars$mpg[mtcars$am==1]
mpg_0<-mtcars$mpg[mtcars$am==0]
var.test(mpg_0,mpg_1)

## 
##  F test to compare two variances
## 
## data:  mpg_0 and mpg_1
## F = 0.38656, num df = 18, denom df = 12, p-value = 0.06691
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.1243721 1.0703429
## sample estimates:
## ratio of variances 
##          0.3865615

3. 성과변수 정규성 + 분산의 동질성 확인에 따른 최종 가설검정

t.test(mtcars$mpg~mtcars$am,mtcars,var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  mtcars$mpg by mtcars$am
## t = -4.1061, df = 30, p-value = 0.000285
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -10.84837  -3.64151
## sample estimates:
## mean in group 0 mean in group 1 
##        17.14737        24.39231