Linear regression (lm) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fit a linear model predicting mpg from hp.

R Programming

model <- lm(mpg ~ [1], data = mtcars)

Drag options to blanks, or click blank then click option'

Ahp

Bmpg

Ccyl

Dwt

Attempts:

3 left

💡 Hint

Common Mistakes

Using the response variable 'mpg' as predictor.

Using unrelated variables like 'cyl' or 'wt' without instruction.

The formula mpg ~ hp fits a linear model predicting miles per gallon (mpg) using horsepower (hp) as the predictor.

2fill in blank

medium

Complete the code to view the summary of the linear model.

R Programming

summary([1])

Drag options to blanks, or click blank then click option'

Amodel

Bmtcars

Clm

Dsummary

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the dataset 'mtcars' instead of the model.

Passing the function name 'lm' or 'summary' itself.

The summary() function shows details about the fitted linear model object named model.

3fill in blank

hard

Fix the error in the code to extract coefficients from the model.

R Programming

coef <- [1](model)

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Acoefficients

Bsummary

Ccoef

Dcoeff

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'summary' which returns a summary object, not coefficients directly.

Using 'coefficients' or 'coeff' which are not functions.

The function coef() extracts the coefficients from a fitted model object.

4fill in blank

hard

Fill both blanks to create a new data frame with predicted mpg values.

R Programming

new_data <- data.frame(hp = c(100, 150, 200))
predictions <- predict([1], newdata = [2])

Drag options to blanks, or click blank then click option'

Amodel

Bnew_data

Cmtcars

Dlm

Attempts:

3 left

💡 Hint

Common Mistakes

Using the dataset 'mtcars' instead of new data for predictions.

Using 'lm' instead of the model object.

Use the fitted model object 'model' and the new data frame 'new_data' to get predictions.

5fill in blank

hard

Fill all three blanks to create a scatter plot with regression line.

R Programming

plot(mtcars$hp, mtcars$mpg, xlab = [1], ylab = [2], main = [3])
abline(model, col = "red")

Drag options to blanks, or click blank then click option'

A"Horsepower"

B"Miles per Gallon"

C"MPG vs Horsepower"

D"Weight"

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up x and y labels.

Using unrelated labels like 'Weight'.

The x-axis label is 'Horsepower', y-axis label is 'Miles per Gallon', and the plot title is 'MPG vs Horsepower'.