Linear Regression Review

Which of the following statements is not true of the correlation r between the lengths in inches and weights 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.

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.9

Which is TRUE?
I.  Random scatter in the residuals indicates a linear model.
II.  If two variables are very strongly associated, then the correlation between them will be near 1 or -1.
III.  Changing the units of measurement for x or y changes the correlation coefficient.

• I only
• II only
• I and II only
• I, II and III

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

CCSS.HSS.ID.C.9

What should a residual plot look like?

• Distinct Pattern
• Curve
• Random with no pattern
• A few clumps

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CCSS.HSS.ID.B.6

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

• YES
• NO

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CCSS.HSS.ID.B.6

Describe the correlation in the graph shown.

• Strong Negative
• Strong Positive
• Weak Negative
• Weak Positive

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CCSS.HSF-LE.A.1B

Which is the best CONTEXTUAL description of the y - intercept for the linear regression equation of the rats: Weight = 100 + 40(Time)?

• The y-intercept is 100, which means the rat is predicted to weigh 100 grams at birth. 
• The y-intercept is 100, which is where the graph crosses the y-axis. 
• 100 is where the data starts on the y-axis. 
• The y-intercept is 100, which means at a weight of 0, the rat will take 100 seconds.

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CCSS.HSF.LE.B.5

Estimate the correlation coefficient for this scatterplot.

• r = 0.56

• r = -0.56

• r = 0.92

• r = -0.92

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CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

The correlation coefficient measures...

• whether there is a relationship between two variables.
• the strength of the relationship between two quantitative variables.
• whether a cause and effect relation exists between two variables.
• the strength of the linear relationship between two quantitative variables.

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CCSS.HSF-LE.A.1B

In a statistics course, a linear regression equation was computed to predict the final exam score based on the score on the first test of the term. The equation was y=25+0.7x where y is the final exam score and x is the score on the first test. George scored 80 on the first test. On the final exam George scored 85. What is the value of his residual?

• -4

• 4

• 4.5

• 5

• 81

Interpret the slope of the Least Squares Line Equation.

• Income increases by 31.45 with each additional 2 hours worked.
• Income increases by 31.45
• As hours increases by one, income will increase by 31.45.
• As hours increases by one, income will decrease by 242.3.

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CCSS.HSS.ID.C.7