title: “Understanding the Effectiveness of Advertising usint t-test” author: “Alexis Blanco taught by zhenning Jimmy xu” date: “9/18/2025” output: html_document editor_options: chunk_output_type: console —
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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 Sun Sep 21 20:20:05 2025"name <- "Solafa Harb"
state <- "California"
print(name) #this syntax is more intuitive## [1] "Solafa Harb"paste(name, "lives in", state)## [1] "Solafa Harb 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 ëbeforeí (let consumers be exposed to natural themes) and ëafterí(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
str(data)## 'data.frame': 30 obs. of 6 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Sales : int 222 201 247 169 317 227 214 187 188 275 ...
## $ price : num 9.83 9.72 10.15 10.04 8.38 ...
## $ ad_type : int 0 1 1 0 1 0 1 0 1 0 ...
## $ price_apple : num 7.36 7.43 7.66 7.57 7.33 7.51 7.57 7.66 7.39 8.29 ...
## $ price_cookies: num 8.8 9.62 8.9 10.26 9.54 ...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 9.49tail(data) #view the last 6 lines## X Sales price ad_type price_apple price_cookies
## 25 25 335 8.34 1 8.23 9.13
## 26 26 145 10.27 0 7.41 10.58
## 27 27 201 10.26 1 7.67 9.22
## 28 28 131 10.49 0 7.59 10.43
## 29 29 210 10.36 0 7.93 9.44
## 30 30 279 8.56 1 7.65 10.44#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. :10.580Descriptive 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')
#Let's perform a histogram analysis
hist(data$price_apple,main='histogram price apple',xlab='price_apple',prob=T)
lines(density(data$price_apple),lty='solid',lwd=2.5, col='red')
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$Sales)## [1] 186.6667mean(sales_ad_family$Sales)## [1] 246.6667Assumption 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 = 0.4155shapiro.test(sales_ad_family$Sales)##
## Shapiro-Wilk normality test
##
## data: sales_ad_family$Sales
## W = 0.89743, p-value = 0.08695t-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.
ìmu=0î refers to the null hypothesis that the difference between Control and Treated is 0, and hence they are similar. alt=ìtwo.sidedî 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 246.6667library(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))| 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 |
``` 1. I Alexis have acknowledge that I read the required readings before approaching this discussion.
- Please read the following article or a different article and list at least 3 reasons R programming might add value to your career.
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..
R programming adds strong value to a career for several reasons. First, it allows automation and reproducibility of data analysis, unlike Excel where steps are often repeated manually. Second, R integrates with powerful visualization libraries like ggplot2, creating professional graphics for presentations. Third, it scales better with large datasets, while Excel struggles with memory limitations. These advantages make R a tool not only for academic work but also for real-world applications in marketing, finance, and research.
- Watch the video “A/B Testing: Test Your Own Hypotheses & Prepare to be Wrong” (see this module) and write a 60-word summary.
The video A/B Testing: Test Your Own Hypotheses & Prepare to be Wrong emphasizes that experiments help businesses test ideas in real time. It explains that A/B testing randomly assigns users into groups, exposing each to different conditions (like ads or website designs) to measure responses. The speaker highlights that we must embrace being wrong, since experiments often disprove assumptions. Ultimately, A/B testing creates evidence-based decisions instead of relying on intuition or biased judgments.
- Please interpret the boxplots for sales data using plain English language? Which graph do you like better? Why?
The boxplots for sales data illustrate the spread and distribution of grape juice sales under two ad types. The “natural production” ad shows a certain central tendency, while the “family health” ad has a slightly higher median and tighter interquartile range. In plain English, the family ad seems more consistent and effective in driving higher sales. I prefer the ggplot2 boxplot with jitter because it is clearer, visually appealing, and easier to interpret than the base R plot
- We performed a Welch’s t-test in step 3. What is your conclusion?
After performing the Welch’s t-test, the results indicate whether there is a statistically significant difference between sales under natural versus family advertisements. Because Welch’s test does not assume equal variances, it is more reliable than Student’s t-test in this scenario. The test results show that family-themed ads generate higher sales with significance, rejecting the null hypothesis. Therefore, I conclude that advertisement type does affect consumer behavior, with family health themes outperforming natural production themes
- 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?
From the descriptive statistics, family-themed ads produced a higher mean sales figure compared to natural production ads. The sales data also appeared roughly normal with no extreme outliers, supporting the validity of the t-test. This descriptive evidence reinforces the Welch’s t-test findings. My conclusion is that in-store advertisements emphasizing family health are more effective. They likely connect with consumer values around care and trust, influencing purchase decisions more strongly than the natural production framing
- Your weekly BlueSky social media reflection (1-min essay) - what did you learn? What do you want to learn more about?
This week, I learned how statistical testing connects directly with marketing decisions. Using R programming and t-tests, I saw how data determines whether an ad campaign is effective rather than relying only on opinions. I also reflected on how visualization helps in communicating insights clearly. What I want to learn more about is applying A/B testing and R analytics to digital marketing campaigns in real time, especially using tools like social media datasets.