Ap Statistics Unit 2 MC Questions

The Dallas Mavericks won the NBA championship in the 2010-2011 season. The two-way table displays the relationship between the outcome of the game in the regular season and whether the Mavericks scored at least 100 points. Which of the following is the best evidence that there is an association between the outcome of a game and whether or not the Mavericks scores at least 100 points?

• The Mavericks won 57 games and lost only 25 games.
• The Mavericks scored at least 100 points in 47 games and fewer than 100 points in only 35 games.
• The Mavericks won 43 games when scoring at least 100 points and only 14 games when scoring fewer than 100 points.
• The Mavericks won a higher proportion of games when scoring at least 100 points (43/47) than when they scored fewer than 100 points (14/35).
• The combination of scoring 100 or more points and winning the game occured more often (43 times) than any other combination of outcomes.

You have data for many years on the average price of a barrel of oil and the average retail price of a gallon of unleaded regular gasoline. If you want to see how well the price of oil predicts the price of gas, then you should make a scatterplot with ___________ as the explanatory variable.

• the price of oil
• the price of gas
• the year
• either oil price or gas price
• time

Which of the following is not a characteristic of the least-squares regression line?

• The slope of the least-squares regression line is always between -1 and 1.
• The least-squares regression line always goes through the point (x-bar,y-bar).
• The least-squares regression line minimizes the sum of squared residuals.
• The slope of the least-squares regression line will always have the same sign as the correlation.
• The least-squares regression line is not resistant to outliers.

Each year, students in an elementary school take a standardized math test at the end of the school year. For a class of fourth-graders, the average score was 55.1 with a standard deviation of 12.3. In the third grade, these same students had an average score of 61.7 with a standard deviation of 14.0. The correlation between the two sets of scores is r = 0.95. Calculate the equation of the least-squares regression line for predicting a fourth- grade score from a third-grade score.

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• Cannot be calculated without the data.

Using data from the LPGA tour, a regression analysis was performed using x = average driving distance and y = scoring average. Using the output from the regression analysis shown below, determine the equation of the least-squares regression line.

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Measurements on young children in Mumbai, India, found this least- squares line for predicting y = height (in cm) from x = arm span (in cm): By looking at the equation of the least-squares regression line, you can see that the correlation between height and arm span is

• greater than zero.
• less than zero.
• 0.93.
• 6.4.
• Can't tell without seeing the data.

Measurements on young children in Mumbai, India, found this least- squares line for predicting y = height (in cm) from x = arm span (in cm): In addition to the regression line, the report on the Mumbai measurements says that r^2 = 0.95. This suggests that

• although arm span and height are correlated, arm span does not predict height very accurately.
• height increases by √0.95 = 0.97 cm for each additional centimeter of arm span.
• 95% of the relationship between height and arm span is accounted for by the regression line.
• 95% of the variation in height is accounted for by the regression line with x = arm span.
• 95% of the height measurements are accounted for by the regression line with x = arm span.

Measurements on young children in Mumbai, India, found this least- squares line for predicting y = height (in cm) from x = arm span (in cm): One child in the Mumbai study had height 59 cm and arm span 60 cm. This child's residual is

• -3.2 cm
• -2.2 cm
• -1.3 cm
• 3.2 cm
• 62.5 cm

Measurements on young children in Mumbai, India, found this least- squares line for predicting y = height (in cm) from x = arm span (in cm): Suppose that a tall child with arm span 120 cm and height 118 cm was added to the sample used in this study. What effect will this addition have on the correlation and the slope of the least-squares regression line?

• Correlation will increase, slope will increase.
• Correlation will increase, slope will stay the same.
• Correlation will increase, slope will decrease.
• Correlation will stay the same, slope will stay the same.
• Correlation will stay the same, slope will increase.

Measurements on young children in Mumbai, India, found this least- squares line for predicting y = height (in cm) from x = arm span (in cm): Suppose that the measurements of arm span and height were converted from centimeters to meters by dividing each measurement by 100. How will this conversion affect the values of r^2 and s?

• r^2 will increase, s will increase
• r^2 will increase, s will stay the same
• r^2 will increase, s will decrease
• r^2 will stay the same, s will stay the same
• r^2 will stay the same, s will decrease