AP Stats -Unit 3 Review

Other things being equal, larger automobile engines are less fuel efficient. You are planning an experiment to study the effect of engine size (in liters) on the fuel efficiency (in mpg) of sport utility vehicles. In this study

• gas mileage is a response variable, and you expect to find a negative association.

• gas mileage is a response variable, and you expect to find a positive association.

• gas mileage is an explanatory variable, and you expect to find a strong negative association.

• gas mileage is an explanatory variable, and you expect to find a strong positive association.

• gas mileage is an explanatory variable, and you expect to find very little association.

Tags

CCSS.HSF-LE.A.1B

For children between the ages of 18 months and 29 months, there is an approximately linear relationship between height and age. The relationship can be represented by ŷ = 64.93 + 0.63x, where y represents height (in centimeters) and x represents age (in months).

Joseph is 22.5 months old. What is his predicted height?

• 50.8

• 64.96

• 65.96

• 79.11

• 87.4

Tags

CCSS.HSF.IF.A.1

CCSS.HSF.IF.A.2

CCSS.HSF.IF.B.4

CCSS.HSF.LE.A.1

For children between the ages of 18 months and 29 months, there is an approximately linear relationship between height and age. The relationship can be represented by ŷ = 64.93 + 0.63x, where y represents height (in centimeters) and x represents age (in months).

Loretta is 20 months old and is 80 cm tall . What is her residual?

• -2.47

• 2.47

• -12.6

• 12.6

• 77.53

Tags

CCSS.8.EE.C.8C

A LSRL for predicting weights of basketball players on the basis of their heights produced the residual plot below. What does the residual plot tell you about the linear model? (click on the picture to enlarge)

• A residual plot is not an appropriate means for evaluating a linear model.

• The curved pattern in the residual plot suggests that there is no association between the weight and height of basketball players.

• The curved pattern in the residual plot suggests that the linear model is not appropriate.

• There are not enough data points to draw any conclusions from the residual plot.

• The linear model is appropriate, because there are approximately the same number of points above and below the horizontal line in the residual plot.

An agricultural economist says that the correlation between corn prices and soybean prices is r = 0.7. This means that

• when corn prices are above average, soybean prices also tend to be above average.

• there is almost no relation between corn prices and soybean prices.

• when corn prices are above average, soybean prices tend to be below average.

• when soybean prices go up by 1 dollar, corn prices go up by 70 cents.

• the economist is confused, because correlation makes no sense in this situation.

Tags

CCSS.HSF.LE.B.5

You are interested in predicting the cost of heating houses on the basis of how many rooms the house has. A scatterplot of 25 houses reveals a strong linear relationship between these variables, so you calculate a least-squares regression line. “Least-squares” refers to

• Minimizing the sum of the squares of the 25 houses’ heating costs.

• Minimizing the sum of the squares of the number of rooms in each of the 25 houses.

• Minimizing the sum of the products of each house’s actual heating costs and the predicted heating cost based on the regression equation.

• Minimizing the sum of the squares of the difference between each house’s heating costs and number of rooms.

• Minimizing the sum of the squares of the residuals.

Tags

CCSS.8.EE.C.8C

The police department tracked the number of ticket writers and number of tickets issued for the past 8 weeks.  The scatter plot shows the results.  Which statement is true?

• More ticket writers results in fewer tickets issued.
• There were 50 tickets issued every week.
• When there are 10 ticket writers, there will be 800 tickets issued.
• More ticket writers results in more tickets issued.

Tags

CCSS.HSF.LE.B.5

We wish to investigate the relationship between the number seeds planted in a garden and the number of fruit grown. The explanatory variable (EV) is

• The number of fruit grown

• The amount of soil in the garden

• The number of hours spent watering the plants

• The number of seeds planted

Tags

CCSS.6.EE.C.9

Which of the following is true?

• We use the response variable to explain changes that might be observed in the explanatory variable.

• The explanatory variable is the variable we expect to be explained by the response variable

• The response variable is also known as the predictor variable

• The explanatory variable is used to explain or predict the value of the response variable

Tags

CCSS.HSS.ID.C.7

Would you say there is a correlation between the two variables? If so, is it strong or weak?

• No correlation. Weak.

• No correlation. Strong.

• Yes Correlation. Weak.

• Yes Correlation. Strong.

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8