Linear Regression Analysis and Visualization

# File Name: linear_regression_analysis.R

# Author: Gerard King - www.gerardking.dev

# Title: Linear Regression Analysis and Visualization

# Description: This R program generates a synthetic dataset, fits a linear regression model,

# and visualizes the data points along with the fitted regression line. It also outputs

# the regression coefficients and other model statistics.

# Use Cases:

# - Predictive modeling for trend analysis

# - Exploring the relationship between two variables

# - Teaching statistical concepts like linear regression

# Audience:

# - Data scientists and analysts

# - Students learning machine learning and statistics

# - Researchers conducting regression analysis

# Blue Team Uses:

# - Analyzing trends in system performance over time

# - Predicting potential future system loads or network traffic

# - Evaluating the correlation between variables in security logs

# Red Team Uses:

# - Modeling potential attack patterns based on historical data

# - Simulating data trends for penetration testing scenarios

# - Assessing the impact of a security breach on system performance

# Current Date: 2025-03-06

# Generate synthetic data for linear regression

set.seed(456)

# Simulate independent variable (x) and dependent variable (y)

x <- rnorm(100)

y <- 2 * x + rnorm(100)

# Create a data frame

data <- data.frame(x = x, y = y)

# Fit a linear regression model

model <- lm(y ~ x, data = data)

# Create a scatter plot of the data points

plot(data$x, data$y, main="Linear Regression: y = 2x + Noise", 

     xlab="X", ylab="Y", pch=19, col="blue")

# Add the regression line to the plot

abline(model, col="red", lwd=2)

# Display the regression coefficients

cat("Regression Coefficients:\n")

print(coef(model))

# Display the summary of the model

cat("\nModel Summary:\n")

summary(model)

# Print the current date for reference

cat("Date of execution:", Sys.Date(), "\n")