Hypothesis:

H0: There is no relationship between the number of (laptops) purchased and the number of anti-virus licenses purchased.

H1: There is a positive relationship between the number of (laptops) purchased and the number of anti-virus licenses purchased.

library(readxl)
dataset <- read_excel("/Users/sharmilaakula/Downloads/OneDrive_2_11-14-2025/A5RQ2.xlsx")
library(psych)
describe(dataset[, c("Antivirus", "Laptop")])
##           vars   n  mean    sd median trimmed   mad min max range  skew
## Antivirus    1 122 50.18 13.36     49   49.92 12.60  15  83    68  0.15
## Laptop       2 122 40.02 12.30     39   39.93 11.86   8  68    60 -0.01
##           kurtosis   se
## Antivirus    -0.14 1.21
## Laptop       -0.32 1.11
hist(dataset$Antivirus,
     main = "Histogram of Antivirus",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 20)

hist(dataset$Laptop,
     main = "Histogram of Laptop",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 20)

shapiro.test(dataset$Antivirus)
## 
##  Shapiro-Wilk normality test
## 
## data:  dataset$Antivirus
## W = 0.99419, p-value = 0.8981
shapiro.test(dataset$Laptop)
## 
##  Shapiro-Wilk normality test
## 
## data:  dataset$Laptop
## W = 0.99362, p-value = 0.8559
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
library(ggpubr)
ggscatter(dataset, x = "Antivirus", y = "Laptop",
          add = "reg.line",
          conf.int = TRUE,
          cor.coef = TRUE,
          cor.method = "pearson",
          xlab = "Antivirus", ylab = "Laptop")

cor.test(dataset$Antivirus, dataset$Laptop, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  dataset$Antivirus and dataset$Laptop
## t = 25.16, df = 120, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.8830253 0.9412249
## sample estimates:
##       cor 
## 0.9168679

Review Your Output

Name of test: Pearson Correlation

Variables: Antivirus and Laptop

Total sample size (n): 122

Statistically significant? Yes

Mean and SD:Antivirus: M = 50.18, SD = 13.36 Laptop: M = 40.02, SD = 12.30

Direction and size: Positive and Strong

Degrees of freedom (df): 120

r-value: 0.92

EXACT p-value: p < .0012)

Final Report

A Pearson correlation was conducted to examine the relationship between anti-virus software purchases and laptop purchases (n = 122). There was a statistically significant correlation between anti-virus software (M = 50.18, SD = 13.36) and laptop purchases (M = 40.02, SD = 12.30). The correlation was positive and strong, r(120) = 0.92, p < .001. As anti-virus software purchases increase, laptop purchases also increase.