Chapter 2 Analyzing Two-Variable Data Practice Test

Which of the following is closest to the correlation between the two variables?

• -0.7

• -0.2

• 0.7

• 0.2

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.9

Which of the following statements is NOT true of the correlation r between the length in inches and the weight in pounds of a sample of brook trout?

• r must take a value between -1 and 1.

• r is measured in inches.

• If longer trout tend to also be heavier, then r > 0.

• r would not change if we measured the lengths of the trout in centimeters instead of inches.

Tags

CCSS.HSF.LE.B.5

What effect would removing the point (8.37, 45) have on the regression line?

• Slope would increase; y intercept would increase.

• Slope would increase; y intercept would decrease.

• Slope would decrease; y intercept would increase.

• Slope would decrease; y intercept would decrease.

Tags

CCSS.HSS.ID.A.3

CCSS.HSS.ID.C.7

• The estimated slope is 6.1, which implies that girls in this population between the ages of 5 and 18 are predicted to grow about 6.1 centimeters in heights for each year they grow older.

• The estimated y-intercept is 50.3 centimeters. We can conclude from the intercept that the typical height of girls in this population at birth is 50.3 cm.

• A 10-year-old girl in this population is at least 110.3 cm tall.

• One particular 8-year-old girl is 115 cm tall. She is shorter than expected for girls her age in this population.

Tags

CCSS.HSS.ID.C.7

• For every 1 degree C increase in temperature, fish activity is predicted to increase by 4.785 units.

• The typical distance of the temperature readings from their mean is about 4.785 degrees C.

• The typical distance of the activity level ratings from the least-squares regression line is about 4.785 units.

• The typical distance of the activity-level ratings from their mean is about 4.785 units.

Tags

CCSS.HSF.LE.B.5

The percent of the variation in the values of y that is accounted for by the least-squares regression of y on x is

• the correlation.

• the slope of the least-squares regression line.

• the square of the correlation.

• the standard deviation of the residuals.

Tags

CCSS.HSS.ID.C.8

• No; a negative correlation would allow that conclusion, but this correlation is positive.

• No; the positive correlation might have arisen because seriously ill people go to large hospitals.

• Yes; the positive correlation establishes that a larger hospital size is the reason for longer stays.

• Yes; but only if r is very close to 1.

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.9

Two modes were calculated on a set of data, a quadratic model and a linear model. The residual plots for each model is shown. Which of the following statements is supported by these residual plots?

• The linear model is a better fit because there is no pattern in the residuals of the linear model.

• The linear model is a better fit because there is an upside-down, U-shaped pattern in the residuals of the linear model.

• The quadratic model is a better fit because there is no pattern in the residuals of the quadratic model.

• The quadratic model is a better fit because there is a U-shaped pattern in the residuals of the quadratic model.

• -340, 294,317.5

• -100,024,682.5

• 100,024,682.5

• 340, 294,317.5

• active students tend to be students with poor grades and vice versa.

• students with good grades tend to be students who are not involved in many extracurricular activities, and vice versa

• students involved in many extracurricular activities are just as likely to get good grades as bad grades; the same is true for students involved in few extra curricularactivities

• there is no linear relationship between number of activities and grade point average for students at this school

Tags

CCSS.HSF-LE.A.1B