AP Statistics Unit 8

Which of these is the best interpretation of the number 0.1297?

• For each additional square foot of area, the price is predicted to increase by about $0.1297

• A house which has 0 square feet is predicted to cost about $0.1297

• For each additional square foot, the price of the house is predicted to increase about $12.97.

• For each additional square foot of area, the price of the house is predicted to increase 129.70.

Which of these is the best interpretation of the number 87.3180?

• The average distance between the price of a house and the mean price of all of the houses is about $87,318.

• Predictions of price based on area using this regression model will be off by about $87,318 on average.

• The sum of the squared deviations for each house is about $87,318.

• There were 87.318 houses in the sample.

Which of the following points has the greatest influence on the strength of the correlation coefficient?

• A

• B

• C

• D

• E

Which of the graphs would have a correlation coefficient of r = 0.69 ?

• 
• 
• 
• 

A health researcher wishes to show that running can help reduce weight. x is the total running time per week (in minutes) and y is the amount of weight loss (in kg). From the regression line given, estimate the amount of weight loss if a person ran for four hours in a week, correct to 2 significant figures.

_____________________ is the use of a regression line for prediction outside the interval of x values used to obtain the line.

A residual...

• the difference between the actual value of y and the value of y predicted by the regression line

• actual y - predicted y

• the difference between the actual value of x and the value of x predicted by the regression line

Shown is a residual plot. Would a linear regression model of the data be most appropriate?

• YES

• NO

Estimate the correlation coefficient.

• r = -0.8

• r = -0.5

• r = 0.5

• r = 0.8

The ___________________ measures the percent of the variability in the response variable that is accounted for by the least-squares regression line.

• coefficient of determination

• coefficient of relaxation