Interpret the slope -6.0.

Answer: For each additional 1000 lb, predicted MPG decreases by 6 Slope is the predicted change in MPG (y) per one-unit (1000 lb) increase in x.

Predict MPG for a car weighing 3,000 lb.

Answer: 27 y-hat = 45 - 6.0(3.0) = 45 - 18 = 27 MPG.

The intercept -120 lb is best described as:

Answer: Not meaningful in context because height = 0 is far outside the data range Extrapolating to x = 0 (zero inches tall) lies outside the observed range, so the intercept has no real-world meaning here.

AP Statistics · Topic 2.6

Linear Regression Models Practice

Part of Exploring Two-Variable Data.(DAT-1.C)

Practice questions

Want a predicted score for the whole AP STAT exam? Take the 20-question diagnostic and Lumi will plan the rest.

Take diagnosticarrow_forward Practice all 2.6

Sample questions

5 of 17 — sign in to practice the rest with adaptive difficulty and mastery tracking.

Sample 1difficulty 2/5
$The regression predicts MPG from weight (in 1000 lb units).$
$Interpret the slope -6.0.$
- A
  $For each additional MPG, weight decreases by 6$
- B
  $For each additional 1000 lb, predicted MPG decreases by 6$
  check_circle
- C
  $MPG decreases by 6% per pound$
- D
  $Heavier cars get 6 MPG$
Why
$Slope is the predicted change in MPG (y) per one-unit (1000 lb) increase in x.$
View full solution & statsarrow_forward
Sample 2difficulty 2/5
$A regression predicts highway MPG from car weight in thousands of pounds.$
$Predict MPG for a car weighing 3,000 lb.$
- A
  $39$
- B
  $27$
  check_circle
- C
  $21$
- D
  $33$
Why
$y-hat = 45 - 6.0(3.0) = 45 - 18 = 27 MPG.$
View full solution & statsarrow_forward
Sample 3difficulty 2/5
$A regression predicts weight from height.$
$The intercept -120 lb is best described as:$
- A
  $The predicted weight when x = 60 inches$
- B
  $The slope of the line$
- C
  $Not meaningful in context because height = 0 is far outside the data range$
  check_circle
- D
  $The mean weight in the sample$
Why
$Extrapolating to x = 0 (zero inches tall) lies outside the observed range, so the intercept has no real-world meaning here.$
View full solution & statsarrow_forward
Sample 4difficulty 2/5
$For a least-squares regression, x-bar = 10 and y-bar = 25.$
$What does the line predict at x = 10?$
- A
  $0$
- B
  $10$
- C
  $Cannot be determined$
- D
  $25$
  check_circle
Why
$The least-squares line passes through (x-bar, y-bar), so it predicts y-bar at x-bar.$
View full solution & statsarrow_forward
Sample 5difficulty 2/5
$The y-intercept a in ŷ = a + bx is$
- A
  $Always 0$
- B
  $Slope of line$
- C
  $Mean of x$
- D
  $Predicted y when x = 0$
  check_circle
Why
$Intercept = predicted y at x = 0 (interpret cautiously if x = 0 is far from data).$
View full solution & statsarrow_forward

Predict my AP scorearrow_forward