create a linear regression in r

In order to create a linear regression in R, you'll first need to load your data into a data frame. Once you have that, you can use the lm() function to fit a linear regression model to your data. Here's an example of how to do so:

main.r
# Load data into a data frame
my_data <- read.csv("my_data.csv")

# Fit a linear regression model
model <- lm(y ~ x1 + x2 + x3, data = my_data)

# Print the model summary
summary(model)
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In this example, replace my_data.csv with the name of your data file and y, x1, x2, and x3 with the names of your dependent and independent variables.

The lm() function creates a linear model using the formula provided. In this case, we are regressing y onto the x1, x2, and x3 variables in our data frame (my_data). We are also saving this model to a variable called model.

Lastly, we can print the summary of the model using the summary() function. This gives us important details about the model quality, including the coefficients and p-values of each predictor variable, as well as the R-squared value and F-statistic of the overall model.