The lm function in the stats R package allows users to fit linear regression models. This function uses ordinary least squares (OLS) to estimate the parameters...
The lm function in the stats R package allows users to fit linear regression models. This function uses ordinary least squares (OLS) to estimate the parameters of a linear regression model, such as the slope (beta) and intercept (alpha) of the relationship between the dependent and independent variables.
The syntax of the lm function is as follows:
lm(y ~ x, data)
where:
y: The dependent variable
x: The independent variable
data: The data frame containing the dependent and independent variables
The lm function will return an object of class "lm", which contains the estimated coefficients, standard errors, p-values, and other information about the model fit.
For example, the following code fits a linear regression model between the "mpg" (fuel efficiency) and "cyl" (number of cylinders) variables in the "mtcars" dataset:
lm(mpg ~ cyl, data = mtcars)
The output from this code will be an lm object, which can be used to access the estimated coefficients, standard errors, and other information about the model.
The following is a more detailed example of using the lm function:
data(mtcars)
model <- lm(mpg ~ cyl, data = mtcars)
summary(model)
plot(model)