R Program to Perform Linear Regression

1. Introduction

Linear regression is a statistical method used to model and analyze the relationships between a dependent variable and one or more independent variables. The main goal of linear regression is to find the best fit straight line that accurately predicts the output values within a range. In this guide, we will explore how to perform linear regression in R.

2. Program Overview

The program will:

1. Create two vectors: one for the dependent variable (y) and another for the independent variable (x).

2. Perform linear regression using the lm() function.

3. Display the summary of the linear regression model.

3. Code Program

# Create two vectors
x <- c(1, 2, 3, 4, 5)        # Independent variable
y <- c(2, 4, 5, 4, 5)       # Dependent variable

# Perform linear regression
linear_model <- lm(y ~ x)

# Display the summary of the model
print(summary(linear_model))

Output:

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max
-0.8000 -0.6000 -0.2000  0.4000  1.2000

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   2.2000     0.9798   2.246   0.0768 .
x             0.6000     0.2945   2.038   0.0997 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8367 on 3 degrees of freedom
Multiple R-squared:  0.5714,	Adjusted R-squared:  0.4286
F-statistic: 4.153 on 1 and 3 DF,  p-value: 0.09973

4. Step By Step Explanation

1. We begin by creating two vectors, "x" and "y". The vector "x" serves as our independent variable, and "y" is the dependent variable. These vectors represent hypothetical data points for this example.

2. Next, we use R's lm() function to perform linear regression. The formula 'y ~ x' indicates that we're modeling 'y' based on 'x'. The result of the lm() function is a linear model object, which we store in the variable "linear_model".

3. Finally, we print the summary of the linear regression model using the summary() function. This provides a comprehensive overview of the model, including coefficients, t-values, and other statistical measures.

Note: The exact values in the output, especially the statistical measures, will vary based on the data points provided in vectors "x" and "y".