Research Scenario 4

A clothing company recently launched a marketing campaign featuring a famous actor. The goal was to increase profits (USD) by associating the brand with a well-liked celebrity. After the campaign, the company wants to determine if the campaign was effective. The company has data for 60 clothing stores. Did the sales increase after the campaign?

Purpose:

Used to test if there is a difference between Before scores and After scores (comparing the means).

NULL HYPOTHESIS (H0)

There is no difference in store sales before and after the marketing campaign.

ALTERNATE HYPOTHESIS (H1)

There is a difference in store sales before and after the marketing campaign.

IMPORT EXCEL FILE

Import your Excel dataset into R to conduct analyses.

INSTALL AND LOAD REQUIRED PACKAGE

chooseCRANmirror(graphics = FALSE, ind = 1) 
install.packages("readxl")

## Installing package into 'C:/Users/manit/AppData/Local/R/win-library/4.5'
## (as 'lib' is unspecified)

## package 'readxl' successfully unpacked and MD5 sums checked

## Warning: cannot remove prior installation of package 'readxl'

## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\manit\AppData\Local\R\win-library\4.5\00LOCK\readxl\libs\x64\readxl.dll
## to C:\Users\manit\AppData\Local\R\win-library\4.5\readxl\libs\x64\readxl.dll:
## Permission denied

## Warning: restored 'readxl'

## 
## The downloaded binary packages are in
##  C:\Users\manit\AppData\Local\Temp\RtmpCMKnXI\downloaded_packages

library(readxl)

IMPORT EXCEL FILE INTO R STUDIO

A6R4<- read_excel("C:\\Users\\manit\\OneDrive\\Desktop\\A6R4.xlsx")

CALCULATE THE DIFFERENCE SCORES

Purpose: Calculate the difference between the Before scores versus the after scores.

Before <- A6R4$PreCampaignSales
After <- A6R4$PostCampaignSales

Differences <- After - Before

HISTOGRAM

Create a histogram for difference scores to visually check skewness and kurtosis.

CREATE THE HISTOGRAMS

hist(Differences,
     main = "Histogram of Difference Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     breaks = 20)

QUESTION 1: Is the histograms symmetrical, positively skewed, or negatively skewed?
ANSWER: The histogram is positively skewed.

QUESTION 2: Did the histogram look too flat, too tall, or did it have a proper bell curve?
ANSWER:The histogram does not follow a proper bell curve.proper bell-shaped curve.

SHAPIRO-WILK TEST

Check the normality for the difference between the groups.

shapiro.test(Differences)

## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.94747, p-value = 0.01186

QUESTION 1: Was the data normally distributed or abnormally distributed?
ANSWER:The data is abnormally distributed because p < .05.

BOXPLOT

Check for any outliers impacting the mean.

boxplot(Differences,
        main = "Distribution of Score Differences (After - Before)",
        ylab = "Difference in Scores",
        col = "blue",
        border = "darkblue")

QUESTION 1: How many dots are in your boxplot?
Ans)One or two dots.

QUESTION 2: Where are the dots in your boxplot?
Ans)The dots are Far away from the whiskers.

QUESTION 3: Based on the dots and there location, is the data normal?
Ans) From the boxplot, I can see one outlier sitting far above the whiskers. Since this point is clearly separated from the rest of the data, it means the distribution isn’t really normal. A normal dataset would have no outliers or maybe one or two very close to the whiskers. But here, the outlier is too extreme, so the data is not normal.

DESCRIPTIVE STATISTICS

DESCRIPTIVES FOR BEFORE SCORES

mean(Before, na.rm = TRUE)

## [1] 25154.53

median(Before, na.rm = TRUE)

## [1] 24624

sd(Before, na.rm = TRUE)

## [1] 12184.4

length(Before)

## [1] 60

DESCRIPTIVES FOR AFTER SCORES

mean(After, na.rm = TRUE)

## [1] 26873.45

median(After, na.rm = TRUE)

## [1] 25086

sd(After, na.rm = TRUE)

## [1] 14434.37

length(After)

## [1] 60

WILCOXON SIGN RANK TEST

wilcox.test(Before, After, paired = TRUE)

## 
##  Wilcoxon signed rank test with continuity correction
## 
## data:  Before and After
## V = 640, p-value = 0.0433
## alternative hypothesis: true location shift is not equal to 0

DETERMINE STATISTICAL SIGNIFICANCE

If results were statistically significant (p < .05), continue to effect size section below.If results were NOT statistically significant (p > .05), skip to reporting section below.

EFFECT SIZE FOR WILCOXON SIGN RANK TEST

Purpose: Determine how big of a difference there was between the group means.

INSTALL REQUIRED PACKAGE

install.packages("coin")

## Installing package into 'C:/Users/manit/AppData/Local/R/win-library/4.5'
## (as 'lib' is unspecified)

## package 'coin' successfully unpacked and MD5 sums checked

## Warning: cannot remove prior installation of package 'coin'

## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\manit\AppData\Local\R\win-library\4.5\00LOCK\coin\libs\x64\coin.dll to
## C:\Users\manit\AppData\Local\R\win-library\4.5\coin\libs\x64\coin.dll:
## Permission denied

## Warning: restored 'coin'

## 
## The downloaded binary packages are in
##  C:\Users\manit\AppData\Local\Temp\RtmpCMKnXI\downloaded_packages

install.packages("rstatix")

## Installing package into 'C:/Users/manit/AppData/Local/R/win-library/4.5'
## (as 'lib' is unspecified)

## package 'rstatix' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\manit\AppData\Local\Temp\RtmpCMKnXI\downloaded_packages

LOAD THE PACKAGE

library(coin)

## Loading required package: survival

library(rstatix)

## 
## Attaching package: 'rstatix'

## The following objects are masked from 'package:coin':
## 
##     chisq_test, friedman_test, kruskal_test, sign_test, wilcox_test

## The following object is masked from 'package:stats':
## 
##     filter

CALCULATE RANK BISERIAL CORRELATION (EFFECT SIZE)

df_long <- data.frame(
id = rep(1:length(Before), 2),
time = rep(c("Before", "After"), each = length(Before)),
score = c(Before, After)
)

wilcox_effsize(df_long, score ~ time, paired = TRUE)

## # A tibble: 1 × 7
##   .y.   group1 group2 effsize    n1    n2 magnitude
## * <chr> <chr>  <chr>    <dbl> <int> <int> <ord>    
## 1 score After  Before   0.261    60    60 small

QUESTION

Q1) What is the size of the effect?
Ans)The effect size is 0.26, which is considered a small effect.

Q2) Which group had the higher average score?
Ans)The After group had the higher average score, meaning scores increased after the intervention.

SUMMARY OF RESULTS

A Wilcoxon Signed-Rank Test was conducted to compare the sales before and after a marketing campaign of a clothing company. Median sales were significantly lower before the campaign (Md = 24624) than after (Md = 25086), V = 640, p = 0.0433. These results indicate that the campaign has successfully managed to increase the clothing brand’s sales. The effect size was r = 0.261, indicating a small effect.

Team 6 scene 4

2025-11-21