What are the null and alternate hypotheses for your research?
H0:There is no relationship between number of laptop purchased and anti virus licenses purchased.
H1:There is relationship between number of laptop purchased and anti virus licenses purchased.

INSTALL REQUIRED PACKAGE

library(readxl)

IMPORT THE EXCEL FILE INTO R STUDIO

dataset <- read_excel("C:/Users/burug/Downloads/A5RQ2.xlsx")
library(psych)

CALCULATE THE DESCRIPTIVE DATA

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

CREATE A HISTOGRAM FOR EACH CONTINUOUS VARIABLE

hist(dataset$Antivirus,
     main = "Histogram of V1",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 20)

hist(dataset$Laptop,
     main = "Histogram of V2",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 20)

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?
The Skewness of the variable 1 histogram is positively 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?
The Antivirus histogram looks slightly flat but close to a normal bell curve.
Q3) Check the SKEWNESS of the VARIABLE 2 histogram. In your opinion, does the histogram look symmetrical, positively skewed, or negatively skewed?
The Skewness of the variable 2 histogram is 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?
The Antivirus histogram looks slightly flat but close to a normal bell curve

CONDUCT THE SHAPIRO-WILK TEST

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

Answer the questions below as a comment within the R script:
Q:Was the data normally distributed for Variable 1?
A:Yes, the data for Antivirus is normally distributed
Q:Was the data normally distributed for Variable 2?
A:Yes, the data for Laptop is normally distributed

LOAD THE PACKAGE

library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
library(ggpubr)

CREATE THE SCATTERPLOT

ggscatter(dataset, x = "Antivirus", y = "Laptop",
          add = "reg.line",
          conf.int = TRUE,
          cor.coef = TRUE,
          cor.method = "pearson",
          xlab = "Antivirus", ylab = "Laptop")

Q:Is the relationship positive (line pointing up), negative (line pointing down), or is there no relationship (line is flat)?
A:The relationship between Antivirus and Laptop is positive, indicating that as the number of Antivirus licenses purchased increases, the number of Laptops purchased also tends to increase.

CONDUCT THE PEARSON CORRELATION

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

Q1) What is the direction of the effect?
A1 : The direction of the effect is positive. As the number of Antivirus licenses purchased increases, the number of Laptops purchased also increases.
Q2) What is the size of the effect?
A2 : The size of the effect is strong. The correlation coefficient is r = 0.917, which is close to 1.00 and indicates a strong relationship.

REPORT
A Pearson correlation was conducted to examine the relationship between the number of Antivirus licenses purchased and the number of Laptops purchased (n = 122). There was a statistically significant correlation between Antivirus licenses (M = 50.18, SD = 13.36) and Laptops purchased (M = 40.02, SD = 12.30), r(120) = 0.917, p < .001. The correlation was positive and strong.As the number of Antivirus licenses purchased increases, the number of Laptops purchased also increases.