Linear regression (lm) in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the lm() function in R?

The lm() function fits a linear regression model, which finds the best straight line to describe the relationship between a dependent variable and one or more independent variables.

beginner

In the formula lm(y ~ x, data = df), what does y ~ x mean?

It means we want to predict y (dependent variable) using x (independent variable). The tilde ~ separates the outcome from the predictor.

beginner

What does the coefficient estimate in a linear regression model represent?

It shows how much the dependent variable changes when the independent variable increases by one unit, assuming other variables stay the same.

beginner

How do you check the summary of a linear model in R?

Use the summary() function on the model object, like summary(model). It shows coefficients, significance, and model fit statistics.

intermediate

What does the Residuals section in summary(lm) tell you?

It shows the differences between observed and predicted values. Small residuals mean the model fits the data well.

What does the lm() function in R do?

A Fits a linear regression model

B Creates a scatter plot

C Calculates the mean of a variable

D Performs a t-test

The lm() function fits a linear regression model to data.

In lm(y ~ x, data = df), what is y?

A Data frame name

B Dependent variable

C Independent variable

D Model summary

y is the dependent variable we want to predict.

Which function shows detailed results of a linear model in R?

A lm()

B plot()

C mean()

D summary()

summary() shows coefficients, residuals, and statistics of the model.

What does a coefficient estimate of 2 mean in a simple linear regression?

A For each 1 unit increase in x, y increases by 2 units

B The model has 2 predictors

C The residual is 2

D The intercept is 2

Coefficient 2 means y changes by 2 units for every 1 unit increase in x.

What do residuals represent in a linear regression model?

A Model coefficients

B Predicted values only

C Differences between observed and predicted values

D Independent variables

Residuals are the errors or differences between actual and predicted values.

Explain how to fit a simple linear regression model in R using lm() and interpret the output.

Think about predicting one variable from another and checking the model summary.

Describe what residuals are in linear regression and why they matter.

Residuals tell us how well the model predictions match the actual data.