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Linear Regression Concept

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data.

In simple linear regression, the relationship between the dependent variable y and the independent variable x is represented by the equation:

y = mx + b

Where:

• y is the dependent variable
• x is the independent variable
• m is the slope of the line
• b is the y-intercept

Here's a simple example of linear regression in R:

# Sample Data x <- c(1, 2, 3, 4, 5) y <- c(2, 4, 5, 4, 5) # Fit Linear Model lm_model <- lm(y ~ x) # Print Summary summary(lm_model)

This R code uses the lm() function to fit a linear model to the given data and then prints a summary of the model using summary().

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