#Please install the following package if the package "readr" is not installed.
#install.packages("readr")
library(readr)
data <- read_csv("ab_testing1.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Ads = col_double(),
## Purchase = col_double()
## )ls(data) # list the variables in the dataset## [1] "Ads" "Purchase"head(data) #list the first 6 rows of the dataset## # A tibble: 6 x 2
## Ads Purchase
## <dbl> <dbl>
## 1 1 152
## 2 0 21
## 3 2 77
## 4 0 65
## 5 1 183
## 6 1 87# creating the factor variable
data$Ads <- factor(data$Ads)
is.factor(data$Ads)## [1] TRUE# showing the first 15 rows of the variable "Ads"
data$Ads[1:15]## [1] 1 0 2 0 1 1 2 2 2 0 2 2 0 2 2
## Levels: 0 1 2#now we do the regression analysis and examine the results
summary(lm(Purchase~Ads, data = data))##
## Call:
## lm(formula = Purchase ~ Ads, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -59.75 -22.75 -3.75 30.25 64.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.00 10.21 4.800 5.69e-05 ***
## Ads1 69.71 15.91 4.383 0.000171 ***
## Ads2 24.75 13.82 1.791 0.084982 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.28 on 26 degrees of freedom
## Multiple R-squared: 0.4262, Adjusted R-squared: 0.3821
## F-statistic: 9.656 on 2 and 26 DF, p-value: 0.0007308