AP Stats Worksheet

Which of the following is the best description of the relationship between the mass and the age of the chameleon eggs?

• The association is negative and linear.

• The association is positive and linear.

• There is no association between the variables.

• The association is positive and nonlinear.

• The association is negative and nonlinear.

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.7

What is unusual about point M?

• A) It represents a mosquito that gained the least weight after feeding.

• B) It represents a mosquito that gained the most weight after feeding.

• C) It represents a mosquito that flew very fast after feeding relative to all other mosquitoes.

• D) It makes the linear relationship between the variables appear much stronger.

• E) The point must be an error in data entry because weight cannot be less than 0.

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CCSS.HSS.ID.A.3

If a correlation between the daily high temperature and the pH of the cheese is -.64. What information does the correlation provide about the relationship between the daily high temperature and the pH of the cheese?

• The relationship is linear because the correlation is negative.

• The relationship is not linear because the correlation is negative.

• The morning pH of the cheese tends to be higher when the daily high temperature in the shop is warmer, compared to when the daily high temperature is cooler.

• The morning pH of the cheese tends to be higher when the daily high temperature in the shop is cooler, compared to when the daily high temperature is warmer.

• There is no relationship between the daily high temperature and the pH of the cheese.

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.9

A certain middle school opens at 7 A.M., and classes begin at 8 A.M. A sociologist gathered data on the number of minutes after 7 A.M. that a student arrives at school and the number of friends the student has on social media. The correlation between the two variables indicated a strong negative relationship. Which of the following is an appropriate interpretation of the correlation?

• Students who arrive early to school have more friends on social media.

• Students who arrive late to school have more friends on social media.

• Having a lot of friends on social media causes a student to arrive late for school.

• As the number of minutes of arriving after 7 A.M. increases, the number of friends on social media tends to increase.

• As the number of minutes of arriving after 7 A.M. increases, the number of friends on social media tends to decrease.

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.9

A small business owner has created a linear regression model to predict the number of new customers who will visit a shop based on the number of times the owner has an advertisement played on the radio. What is the explanatory variable and what is the response variable?

• Explanatory: number of new customers; response: number of times the advertisement is played

• Explanatory: number of times the advertisement is played; response: number of new customers

• Explanatory: number of times the advertisement is played; response: number of purchases made by customers

• Explanatory: number of purchases made by customers; response: number of times the advertisement is played

• Explanatory: number of previous customers; response: number of new customers

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CCSS.HSS.ID.B.6

The following is a residual plot from a regression of a variable with the independent variable x. Based on the plot, is it reasonable to conclude that a linear model is appropriate?

• Yes, because the plot shows no apparent pattern.

• Yes, because the points in the plot display less variation as x increases.

• Yes, because the sum of the residuals is close to zero.

• No, because the plot shows no apparent pattern.

• No, because the points in the plot display more variation as x increases.

• A) For each increase of $3 in the price of the sandwich, the number sold is predicted to decrease, on average, by 150.

• B) For each increase of $3 in the price of the sandwich, the number sold is predicted to increase, on average, by 150.

• C) For each increase of $1 in the price of the sandwich, the number sold is predicted to decrease, on average, by 3.

• D) For each increase of $1 in the price of the sandwich, the number sold is predicted to increase, on average, by 3.

• E) For each increase of $1 in the price of the sandwich, the number sold is predicted to decrease, on average, by 150.

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CCSS.HSF.LE.B.5

Researchers are investigating how the amount of monthly rainfall, measured in centimeters (cm), affects the monthly growth, in cm, of a certain plant. From a sample of data, the researchers created a least-squares regression line. Computer output is shown in the following table. Which of the following statements is an interpretation of the value 0.75 shown in the table?

• A) Monthly growth is expected to increase by 0.75 cm when rainfall increases by 1 cm.

• B) Rainfall is expected to increase by 0.75 cm when monthly growth increases by 1 cm.

• C) For a month with 0 cm of rainfall, the monthly growth is expected to be approximately 0.75 cm.

• D) For a plant with 0 cm of monthly growth, the month had an expected rainfall of approximately 0.75 cm.

• E) Approximately 75% of the variability in monthly growth is due to its linear relationship with rainfall.

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CCSS.HSS.ID.C.7

The following scatterplot shows a company's monthly sales, in thousands of dollars, versus monthly advertising dollars spent, in thousands of dollars. Which of the following points is most likely a high-leverage point with respect to a regression of monthly sales versus advertising dollars?

• (5.1, 105)

• (5.8, 110)

• (6.0, 125)

• (6.7, 108)

• (6.8, 123)

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CCSS.HSS.ID.B.6

The following scatterplot shows the ages, in years, of 12 of the wealthiest people in the world along with their net worth, in billions of dollars. The data point at age 83 is labeled Q. Suppose point Q is removed from the data set. Which of the following is likely not affected by the removal?

• A) The correlation coefficient

• B) The sign of the slope coefficient

• C) The value of the slope coefficient

• D) The sum of the squared residuals

• E) The net worth intercept

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CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.7