Practice Residuals in Statistics

Use these practice problems to test your method after reviewing the concept explanation and worked examples.

Quick Recap

A residual is the difference between an observed data value and the value predicted by a statistical model, calculated as residual=yobservedโˆ’ypredicted\text{residual} = y_{\text{observed}} - y_{\text{predicted}}. Positive residuals mean the model underestimated; negative residuals mean it overestimated.

If your model predicts 80 but the actual value is 85, the residual is +5. Residuals are 'leftovers' - what the model couldn't explain. Patterns in residuals reveal model problems.

Showing a random 20 of 76 problems.

Example 1

medium
Using y^=4xโˆ’1\hat{y} = 4x - 1, find the residual at (3,9)(3, 9).

Example 2

medium
y^=0.8x+4\hat{y} = 0.8x + 4. Observed (20,22)(20, 22). Find the residual.

Example 3

medium
The sum of residuals for the least-squares regression line is always what number?

Example 4

medium
For y^=โˆ’x+10\hat{y}=-x+10, find the residual at (2,5)(2, 5) and state over- or under-estimate.

Example 5

medium
For y^=4xโˆ’1\hat{y}=4x-1, find the residual at (2,6)(2, 6) and state whether the point lies above or below the line.

Example 6

medium
If the largest residual is +12+12, what does this point look like on a scatterplot?

Example 7

medium
Using y^=0.5x+2\hat{y} = 0.5x + 2, complete the residual table for (2,3),(4,5),(6,4),(8,7)(2,3),(4,5),(6,4),(8,7).

Example 8

medium
For a least-squares line, what is โˆ‘(yiโˆ’y^i)\sum (y_i - \hat{y}_i)?

Example 9

hard
Sum of squared residuals is 8080 for n=10n = 10 points (regression with 11 predictor). Estimate the residual standard error ss.

Example 10

medium
For y^=2x+3\hat{y} = 2x + 3, find residuals at (1,6),(2,8),(3,10)(1, 6), (2, 8), (3, 10) and identify the worst-fit point.

Example 11

medium
Squaring residuals before summing produces what quantity used to fit the line?

Example 12

medium
Why do we square residuals when fitting the least-squares line?

Example 13

medium
A residual plot shows residuals randomly scattered around 00. What does this suggest?

Example 14

medium
How is a residual different from the model's 'error' term?

Example 15

easy
A point lies above the regression line. Is its residual positive or negative?

Example 16

medium
Two models fit the same point (4,20)(4, 20). Model A predicts 18, Model B predicts 23. Which model fits this point better?

Example 17

medium
Residual plot of a linear fit shows a clear funnel shape (spread grows with x). What does this reveal?

Example 18

medium
Regression line: y^=4xโˆ’1\hat{y} = 4x - 1. Three observed points are (1,5),(2,6),(3,12)(1, 5), (2, 6), (3, 12). Find each residual.

Example 19

medium
A residual plot scatters randomly around zero with no pattern. What does this suggest about the linear model?

Example 20

hard
A regression model predicts y^=30\hat{y} = 30 for a data point, but the actual value is y=35y = 35. Calculate the residual and interpret it.