"","group","prev","post""1","A","194.6","302.3""2","A","330.08","0""3","A","331.16","67.6""4","A","1...

Question

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Pooja · Accepted Answer

**group**: "A" if the user was in the control group, "B" if the user was in the treatment group and hence had their background color changed to cream.
**prev**: numeric variable representing the amount the user spent on Amazon in the month prior to the (possible) color change.
**post**: numeric variable representing the amount the user spent on Amazon in the month after the color change went into effect for group "B". 
You are of course welcome/encouraged to do a preliminary exploration of this data, but this exploration should not be included in your submission (unless relevant to answering one of the questions).
---
##Problem \# 1: A/B testing
Complete the following A/B tests, including computation and interpretation of a p-value. Show your work, including any R code used.
a. Test the difference in means (between group $A$ and group $B$) for the "post" variable.
> t.test(post~group,data=data1)
	Welch Two Sample t-test
data:  post by group
t = 0.4874, df = 14672, p-value = 0.626
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -4.080028  6.780636
sample estimates:
mean in group A mean in group B 
       119.6497        118.2994 
With t=0.4874, p>5%, I fail to reject null hypothesis and conclude that there is no significant difference in the post between group $A$ and group $B$.
b. Test the difference in proportions (between group $A$ and group $B$) of users that increased their spending from prev to post.
> diff  data1$diff  View(data1)
> data1$increase 0, 1, 0)
> addmargins(table1)
     
          A     B   Sum
  0    5872  6795 12667
  1    1128  1205  2333
  Sum  7000  8000 15000
> prop.test(x = c(1128, 1205), n = c(7000, 8000))
	2-sample test for equality of proportions with continuity correction
data:  c(1128, 1205) out of c(7000, 8000)
X-squared = 3.0649, df = 1,

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