Linear Regression in R: A Complete Guide

In R language, you can use the lm() function to fit a linear regression model. Here are the specific steps:

1. Prepare the data: First, have the data ready for fitting, including the independent and dependent variables.
2. Utilize the lm() function: The lm() function is used to fit a linear regression model, with the syntax lm(formula, data), where formula is a formula describing the relationship between the dependent and independent variables, and data is the data frame containing the data.

For example, assuming there is a dataset data containing two variables x and y, you can use the following code to fit a linear regression model of y on x.

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

1. View model results: Use the summary() function to examine the fitting results, which includes estimated parameter values, goodness of fit, and other relevant information.

summary(model)

By following the above steps, you can fit a linear regression model in R and examine the fitting results.