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

Hypotheses

H0:There is no relationship between Antivirus and Laptop.

H1:There is a relationship between Antivirus and Laptop.

Directional: As Antivirus increase, Laptop increase Or decrease

Import Data

dataset <- read_excel("C:/Users/konifade/Downloads/A5RQ2.xlsx")
head(dataset)
## # A tibble: 6 × 3
##   Business Antivirus Laptop
##      <dbl>     <dbl>  <dbl>
## 1        1        42     31
## 2        2        47     36
## 3        3        73     68
## 4        4        51     38
## 5        5        52     43
## 6        6        76     61

Descripitve Statistics

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

Comment

Comment: Antivirus: M = 50.18, SD = 13.36, skew = 0.15, kurtosis = –0.14. Laptop: M = 40.02, SD = 12.30, skew = –0.01, kurtosis = –0.32. Both variables are approximately symmetric.

Normality Check

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-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

Comment

Both variables have p-values > 0.05, indicating they are normally distributed.

Antivirus: W = 0.99419, p-value = 0.8981

Laptop: W = 0.99362, p-value = 0.8559

Scatterplot

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

Comment

The scatterplot shows a strong positive linear relationship between Antivirus and Laptop. The relationship is positive-Line pointing up (As Antivirus increases, Laptop also increases. The line slopes upward from left to right.)

Correlation Analysis

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

Comment

r(120) = 0.92, p < .001

95% CI [0.88, 0.94] This indicates a very strong, statistically significant positive correlation.

Final Report

A Pearson correlation was conducted to examine the relationship between Antivirus and Laptop (n = 122). There was a statistically significant correlation between Antivirus (M = 50.18, SD = 13.36) and Laptop (M = 40.02, SD = 12.30). The correlation was positive and very strong, r(120) = 0.92, p < .001, 95% CI [0.88, 0.94]. As Antivirus scores increase, Laptop scores also increase.