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#Warm-up activity for fun - can you replace my name with yours using the following functions?

paste("Today is", date())

## [1] "Today is Thu Feb 12 01:05:59 2026"

name <- "Jimmy"
state <- "California"
print(name) #this syntax is more intuitive

## [1] "Jimmy"

paste(name, "lives in", state)

## [1] "Jimmy lives in California"

This step is for those of you would like to use R Studio (the desktop version) instead of R Studio Cloud

If you are a first-time R user, please follow the following tips to set up your working directory:

If you are using Windows 7 and it is on your desktop the full path should be something like this: “C:/Users/yourusername/Desktop/grapeJuice.csv”

you need forward slashes in the path, not backslashes (which caught me out when I started using R). So perhaps you should try: data1<- read.table(file=“C:/Users/yourusername/Desktop/grapeJuice.csv”,header=TRUE). Please make sure to replace ‘yourusername’ in the above path with your real username. For instanc, my current user name is zxu3.

Read the tips below if you do not know your username: https://regroove.ca/oh365eh/2015/04/12/how-to-find-your-user-name-on-your-pc/

Watch the following video if you need to learn how to reset your working directory: RStudio Basics: Setting your Working Directory https://www.youtube.com/watch?v=LNw6hzGgyxM

Research design - Review our Research Design tutorial

This is usually the first step of product design or service design for marketing analytics professionals who work for Wayfair, Walmart, Netflix, Booking.com, etc (see the references)

H0- Null hypotheses use as basis for argument but has not yet proven, no difference prediction (all equal).

H1 - Alternative hypotheses statement set-up to establish like new effect compared to existing (e.g new drug is better than the existing standard products).

Introduction

Which type of in-store advertisement is more effective? To answer this question, the marketing team decided to place two types of ads in a pilot store for testing using two themes of juices: one theme is natural production of the juice, and the other theme is family health caring. The goal of this experiment is to see if they can place the better one into all of the stores after the pilot period.

Research design issues

In this study, we analyze the effectiveness of ads on sales using Welch’s independent sample t-test. Here independent means that points (i.e.,customers in this case) do not match up with each other.

Alternatively, for instance, we might perform a paired sample t test in which we could test if a before (let consumers be exposed to natural themes) and after (let consumers be exposed to family themes) condition will affect the sales of each store.

Variable Description

Sales: Total unit sales of the grape juice in one week in a store Price: Average unit price of the grape juice in the week ad_type: The in-store advertisement type to promote the grape juice.ad_type = 0, the theme of the ad is natural production of the juice ad_type = 1, the theme of the ad is family health caring price_apple: Average unit price of the apple juice in the same store in the week price_cookies: Average unit price of the cookies in the same store in the week

Step 1:

Please write a null hypothesis and an alternative hypothesis using the template hypotheses available in the research design module.

Step 2:

Please make your conclusions based on the results in descriptive analysis 3. What is your conclusion?

We performed a normality test in Step - normality check 1. What is your conclusion?

Hint: read the third reference article.

Step 3: Perform a t-test using Excel or R

Step 3.1 Perform a t-test using Excel

In this step, you will be performing a t-test using Excel. Once you get the result, please attach your output Spreadsheet in the discussion forum.

Reference: Excel - Independent samples Welch t test (via data analysis) https://www.youtube.com/watch?v=sHqCrK_FMyY

Step 3.2 (Preparation & debugging)

In this step, you will be performing the t-test again using R and R studio. The goal is to help you document your analysis for future reference.

Please try to perform the analysis using R and Rpubs before the class on Thursday and post your final bugs and errors (or the final URL of your Rpubs page) to receive participation credits toward your final Engagement grade.

Note: For details about “Preparation & debugging,” please read the section “Preparation & debugging” in the Syllabus or the Syllabus page of LMS.

The “Preparation & debugging” process can be frustrating for statistics majors sometimes. Do not be panic!!! I hope you could recognize the challenge as an opportunity for you to build a stronger sense of self. You may find the following testimony by Thomas Mock helpful. Please also try to watch the YouTube video “R Programming Tutorial - Learn the Basics of Statistical Computing” to get familiar with the R basics.

“Within the first month of the course I actually reverted back to doing things in Systat with a GUI as I was so frustrated with not knowing what I was doing in R.”

References:

My R Journey: Thomas Mock https://rfortherestofus.com/2019/09/my-r-journey-thomas-mock/

R Programming Tutorial - Learn the Basics of Statistical Computing: https://www.youtube.com/watch?v=_V8eKsto3Ug

Step 4: Wrap-up - interpret the results

We performed a Welch’s t test in the step 3. What is your conclusion?

Hint: read the first three reference articles. Make sure to cite.

Descriptive Analysis 1:

data <- read.csv('grapeJuice.csv') #read data
head(data)  #view the first 6 lines

##   X Sales price ad_type price_apple price_cookies
## 1 1   222  9.83       0        7.36          8.80
## 2 2   201  9.72       1        7.43          9.62
## 3 3   247 10.15       1        7.66          8.90
## 4 4   169 10.04       0        7.57         10.26
## 5 5   317  8.38       1        7.33          9.54
## 6 6   227  9.74       0        7.51          9.49

#perform some basic descriptive analysis 
summary(data)

##        X             Sales           price           ad_type     price_apple   
##  Min.   : 1.00   Min.   :131.0   Min.   : 8.200   Min.   :0.0   Min.   :7.300  
##  1st Qu.: 8.25   1st Qu.:182.5   1st Qu.: 9.585   1st Qu.:0.0   1st Qu.:7.438  
##  Median :15.50   Median :204.5   Median : 9.855   Median :0.5   Median :7.580  
##  Mean   :15.50   Mean   :216.7   Mean   : 9.738   Mean   :0.5   Mean   :7.659  
##  3rd Qu.:22.75   3rd Qu.:244.2   3rd Qu.:10.268   3rd Qu.:1.0   3rd Qu.:7.805  
##  Max.   :30.00   Max.   :335.0   Max.   :10.490   Max.   :1.0   Max.   :8.290  
##  price_cookies   
##  Min.   : 8.790  
##  1st Qu.: 9.190  
##  Median : 9.515  
##  Mean   : 9.622  
##  3rd Qu.:10.140  
##  Max.   :10.580

Descriptive Analysis 2:

#set the 1 by 2 layout plot window
par(mfrow=c(1,2))
#Check if there are outliers using a boxplot
#Let's perform boxplots in two different ways
boxplot(data$Sales,main="Boxplot for sales data", ylab="Sales")
boxplot(data$Sales,main="Boxplot for sales data", horizontal = TRUE, xlab="Sales")

#Let's perform a histogram analysis
hist(data$Sales,main='histogram plot for sales data',xlab='sales_grape',prob=T)
lines(density(data$Sales),lty='dashed',lwd=2.5, col='blue')

It seems that there is no outlieer and the distribution of the data is roughly normal.

Descriptive analysis 3 - Compare the mean of sales with the two different ad types

#divide the dataset into two sub dataset by ad_type
sales_ad_nature = subset(data,ad_type==0)
sales_ad_family = subset(data,ad_type==1)

#calculate the mean of sales with different ad_type
mean(sales_ad_nature$Sales)

## [1] 186.6667

mean(sales_ad_family$Sales)

## [1] 246.6667

Assumption check 1

The assumptions of t-tests assumes the observations are normally distributed and independent.

#set the 1 by 2 layout plot window
par(mfrow = c(1,2))

# Explore the distribution of the data using histogram
hist(sales_ad_nature$Sales,main="",xlab="sales with nature theme ad",prob=T)
lines(density(sales_ad_nature$Sales),lty="dashed",lwd=2.5,col="red")

hist(sales_ad_family$Sales,main="",xlab="sales with family theme ad",prob=T)
lines(density(sales_ad_family$Sales),lty="dashed",lwd=2.5,col="red")

#set the 1 by 2 layout plot window
par(mfrow = c(1,2))

# boxplot to check if there are outliers in each group
boxplot(sales_ad_family$Sales,horizontal = TRUE, xlab="sales with family theme ad")
boxplot(sales_ad_nature$Sales,horizontal = TRUE, xlab="sales with nature theme ad")

Let’s build a more elegant boxplot with ggplot (the most elegant and aesthetically pleasing graphics framework available)

First we convert the variable ad_type from a numeric to a factor variable

data$ad_type <- as.factor(data$ad_type)
head(data)

##   X Sales price ad_type price_apple price_cookies
## 1 1   222  9.83       0        7.36          8.80
## 2 2   201  9.72       1        7.43          9.62
## 3 3   247 10.15       1        7.66          8.90
## 4 4   169 10.04       0        7.57         10.26
## 5 5   317  8.38       1        7.33          9.54
## 6 6   227  9.74       0        7.51          9.49

# Import the ggplot library
library(ggplot2)
# Wait for the magic to happen
ggplot(data, aes(x=ad_type, y=Sales, fill=ad_type))+
  geom_boxplot(outlier.shape = NA, alpha=.5) +
  geom_jitter(width=.1, size=1) +
  theme_classic() +
  scale_fill_manual(values=c("lightseagreen","darkseagreen"))

Assumption check 2

In this step, we perform a Shapiro test to see if our data is from a normaly distributed population.

shapiro.test(sales_ad_nature$Sales)

## 
##  Shapiro-Wilk normality test
## 
## data:  sales_ad_nature$Sales
## W = 0.94255, p-value = 0.4155

shapiro.test(sales_ad_family$Sales)

## 
##  Shapiro-Wilk normality test
## 
## data:  sales_ad_family$Sales
## W = 0.89743, p-value = 0.08695

t-test

Performing a t-test with which has two categories (e.g., Controlled and Treated) helps us understand if there are differences in the population means between the two groups.

mu=0 refers to the null hypothesis that the difference between Control and Treated is 0, and hence they are similar. alt= two.sided refers to the a two sided t test. conf=0.95 is the confidence interval.

t.test(Sales ~ ad_type, data)

## 
##  Welch Two Sample t-test
## 
## data:  Sales by ad_type
## t = -3.7515, df = 25.257, p-value = 0.0009233
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -92.92234 -27.07766
## sample estimates:
## mean in group 0 mean in group 1 
##        186.6667        246.6667

library(pander)
panderOptions('round',4)
panderOptions('digits',7)
panderOptions('keep.trailing.zeros',TRUE)
panderOptions("table.split.table", Inf) 
pander(t.test(sales_ad_nature$Sales,sales_ad_family$Sales))

Welch Two Sample t-test: `sales_ad_nature$Sales` and `sales_ad_family$Sales`
Test statistic	df	P value	Alternative hypothesis	mean of x	mean of y
-3.7515	25.2571	9e-04 * * *	two.sided	186.6667	246.6667

Google & StackOverflow are definitely the top go-to choices for developers and programmers at any level

For R-related questions, use https://stackoverflow.com/questions/tagged/r

For statistics related questions, use https://stats.stackexchange.com.

Data Science Specialization at John Hopkins University, https://www.coursera.org/specializations/jhu-data-science

Q&A

I acknowledge that I read the required readings before approaching this discussion. Yes
Please read the following article or a different article and list at least 3 reasons R programming might add value to your career. R studio add value by being an Idusrty Versatilyity and cross-sector Demand R is also Intergration with Modern Data Science Workflows,Future proofing your skillset in an AI-Driven Wrold.

Reference: Understanding R programming over Excel for Data Analysis https://www.gapintelligence.com/blog/understanding-r-programming-over-excel-for-data-analysis/

Please find all other discussion questions for this module here: https://rpubs.com/utjimmyx/ttestLinks to an external site..

Watch the video “A/B Testing: Test Your Own Hypotheses & Prepare to be Wrong” (see this module) and write a 60-word summary. A/B testing is a controlled experiment comparing one or more versions of something like a website to determine which performs better. Groups are randomly assigned different versions, and results measure which attracts more audience engagement based on the specific elements changed between versions. This method provides data-driven insights into user preferences and helps optimize design decisions for maximum effectiveness.
Please interpret the boxplots for sales data using plain English language? Which graph do you like better? Why? looking at both box plots they both have the saem data they just look different as one is horozontal and the other is vertical. the horizantal one looks vizualy more readable.
We performed a Welch’s t-test in step 3. What is your conclusion? In conlution the the t-tests looks right skewed confirming a pattern.
Please make your conclusions based on the results of the descriptive analysis. What is your conclusion? Which type of in-store advertisement is more effective? Why? Based on the results in store advertisement seems more effective as the numbers are significantly higher. Ad type 1have median sales of 235 units compared to 180 units without advertising—a 30% increase. The entire advertised distribution sits higher with minimal overlap, proving consistent improvement across all products. Even the worst advertised product outsells typical non-advertised products. Cloud ai

References

Stuart Frisby, Booking.com - Conversions@Google 2017. https://www.youtube.com/watch?v=_sx5LV23hIE

Design Testing at Netflix https://www.youtube.com/watch?v=-Gy8TnoXZf8

Mobile A/B Testing Results Analysis: Statistical Significance, Confidence Level and Intervals https://splitmetrics.com/blog/mobile-a-b-testing-statistical-significance/

Gemini: Wayfair s advanced marketing test design and measurement platform https://tech.wayfair.com/data-science/2019/07/gemini-wayfairs-advanced-marketing-test-design-and-measurement-platform/

Two Independent Samples Unequal Variance (Welch s Test) https://sites.nicholas.duke.edu/statsreview/means/welch/

ANOVA, t-tests and regression: different ways of showing the same thing http://deevybee.blogspot.com/2017/11/anova-t-tests-and-regression-different.html

The Independent Samples t-test (Welch Test) https://stats.libretexts.org/Bookshelves/Applied_Statistics/Book%3A_Learning_Statistics_with_R_-_A_tutorial_for_Psychology_Students_and_other_Beginners_(Navarro)/13%3A_Comparing_Two_Means/13.04%3A_The_Independent_Samples_t-test_(Welch_Test)

https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test