A6Scenario2

QUESTION What are the null and alternate hypotheses for YOUR research scenario? H0: There is no difference between the customer satisfaction scores served by human agents and served by AI chatbots H1: There is a difference between the customer satisfaction scores served by human agents and served by AI chatbots

library(readxl)

A6R2 <- read_excel("C:/Users/hites/Downloads/A6R2.xlsx")

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

A6R2 %>%
  group_by(ServiceType) %>%
  summarise(
    Mean = mean(SatisfactionScore, na.rm = TRUE),
    Median = median(SatisfactionScore, na.rm = TRUE),
    SD = sd(SatisfactionScore, na.rm = TRUE),
    N = n()
  )

## # A tibble: 2 × 5
##   ServiceType  Mean Median    SD     N
##   <chr>       <dbl>  <dbl> <dbl> <int>
## 1 AI           3.6       3  1.60   100
## 2 Human        7.42      8  1.44   100

hist(A6R2$SatisfactionScore[A6R2$ServiceType == "Human"],
     main = "Histogram of Human Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 20)

hist(A6R2$SatisfactionScore[A6R2$ServiceType == "AI"],
     main = "Histogram of AI service Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 12)

QUESTIONS Answer the questions below as comments within the R script:

Q1) Check the SKEWNESS of the VARIABLE 1 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed? Negatively skewed

Q2) Check the KURTOSIS of the VARIABLE 1 histogram. In your opinion, does the histogram look too flat, too tall, or does it have a proper bell curve? Bell curve

Q3) Check the SKEWNESS of the VARIABLE 2 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed? Positively skewed

Q4) Check the KUROTSIS of the VARIABLE 2 histogram. In your opinion, does the histogram look too flat, too tall, or does it have a proper bell curve? Flat

shapiro.test(A6R2$SatisfactionScore[A6R2$ServiceType == "Human"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6R2$SatisfactionScore[A6R2$ServiceType == "Human"]
## W = 0.93741, p-value = 0.0001344

shapiro.test(A6R2$SatisfactionScore[A6R2$ServiceType == "AI"])

## 
##  Shapiro-Wilk normality test
## 
## data:  A6R2$SatisfactionScore[A6R2$ServiceType == "AI"]
## W = 0.91143, p-value = 5.083e-06

QUESTION Answer the questions below as a comment within the R script: Was the data normally distributed for Variable 1? Not Normal

Was the data normally distributed for Variable 2? Not Normal

library(ggplot2)
library(ggpubr)

ggboxplot(A6R2, x = "ServiceType", y = "SatisfactionScore",
          color = "ServiceType",
          palette = "jco",
          add = "jitter")

QUESTION Answer the questions below as a comment within the R script. Answer the questions for EACH boxplot: Q1) Were there any dots outside of the boxplot? Are these dots close to the whiskers of the boxplot (check if there are any dots past the lines on the boxes) or are they very far away? There are multiple dots outside the boxplot. There are some dots away from the whiskers. # If there are no dots, continue with Independent t-test. # If there are a few dots (two or less), and they are close to the whiskers, continue with the Independent t-test. # If there are a few dots (two or less), and they are far away from the whiskers, consider switching to Mann Whitney U test. # If there are many dots (more than one or two) and they are very far away from the whiskers, you should switch to the Mann Whitney U test.

wilcox.test(SatisfactionScore ~ ServiceType, data = A6R2, exact = FALSE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  SatisfactionScore by ServiceType
## W = 497, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

library(effectsize)

rank_biserial(SatisfactionScore ~ ServiceType, data = A6R2, exact = FALSE)

## r (rank biserial) |         95% CI
## ----------------------------------
## -0.90             | [-0.93, -0.87]

QUESTIONS # Answer the questions below as a comment within the R script:

Q1) What is the size of the effect? The rank-biseral correlaion of -0.90 indicates the difference between the groups was large.

Q2) Which group had the higher average rank? The human group had the higher average rank

REPORT

A Mann-Whitney U test was conducted to compare customer satisfaction scores between customer served by human agents (n = 100) and those served by an AI chatbot (n = 100). Customers served by human agents had significantly higher median scores (Mdn = 8.00) than those customers served by AI Chatbots (Mdn = 3.00), U = 497, p = 2.2e-16. The effect size was large (r = -0.90), indicating a meaningful difference between two services. Overall, customers were more satisfied when served by human agents compared to AI Chatbots.