StatTutor Lesson - Facts about the Least-Squares Regression

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Facts about least-squares Line
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      Questions 1-3

      34

      Question 1.

      True or false: Both correlation and linear regression require a straight line relationship between X and Y.

      A.
      B.

      Correct. This is a correct statement.
      Incorrect. This is a correct statement.
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      Questions 4-6

      93

      Question 4.

      True or false: X and Y can be interchanged in both correlation and linear regression.

      A.
      B.

      Correct. X and Y can be interchanged in correlation, but not in the formula for a regression line: y^=a+bx.
      Incorrect. X and Y can be interchanged in correlation, but not in the formula for a regression line:y^=a+bx.
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      Questions 7-11

      173

      Question 7.

      True or false: Slope and correlation (r) always have the same sign.

      A.
      B.

      Correct. This is a correct statement.
      Incorrect. This is a correct statement.
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      Question 12

      198

      Question 12.

      True or false: The regression line always passes through the point (x¯, y¯).

      A.
      B.

      Correct. This is a correct statement.
      Incorrect. This is a correct statement.
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      Questions 13-16

      427

      Question 13.

      Which of the following values for r2 indicate perfect fit (i.e., all data points are on the regression line.)

      A.
      B.
      C.
      D.
      E.

      Correct. When all of the data points are on the regression line, r is either –1.0 or +1.0. So, r2 will be 100%.
      Incorrect. When all of the data points are on the regression line, r is either –1.0 or +1.0. So, r2 will be 100%.
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      Question 17

      502

      Question 17.

      True or false: r2 is a measure of how successfully the regression line explains the variation in y.

      A.
      B.

      Correct. This is a correct statement.
      Incorrect. This is a correct statement.
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      Questions 18-19

      593

      Question 18.

      True or false: If r2 is really close to 100%, then the sum of squared residuals is very small.

      A.
      B.

      Correct. This is a correct statement.
      Incorrect. This is a correct statement.
      2
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      Question 20

      631

      Question 20.

      True or false: r2 measures the fraction of y values that are exactly predicted by the x values.

      A.
      B.

      Correct. r2 is a measure of the fraction of variation in the y’s that is explained by x. It does not tell us the fraction of y values that are exactly predicted as most are not, even when r2 is close to 100%.
      Incorrect. r2 is a measure of the fraction of variation in the y’s that is explained by x. It does not tell us the fraction of y values that are exactly predicted as most are not, even when r2 is close to 100%.
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      Questions 21-22

      754

      Question 21.

      What does total variation in the y’s measure?

      A.
      B.

      Correct. The variability of the y’s about their mean y¯ is called the total variation in y. The variability of the y’s about the regression line is a measure of the prediction errors (or residuals).
      Incorrect. The variability of the y’s about their mean y¯ is called the total variation in y. The variability of the y’s about the regression line is a measure of the prediction errors (or residuals).
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      Questions 23-24

      835

      Question 23.

      Suppose two different explanatory variables have a linear relationship with a response variable, y. The regression line using variable #1 has an r2 of 47% and the regression line using variable #2 has an r2 of 89%. Which explanatory variable explains the most variation in y?

      A.
      B.

      Correct. r2 tells us the percentage of variation in y that is explained by x. And the closer r2 is to 100%, the greater the variation in y that gets explained. So an r2 of 89% tells us that more variation in y is explained than when r2 is 47%.
      Incorrect. r2 tells us the percentage of variation in y that is explained by x. And the closer r2 is to 100%, the greater the variation in y that gets explained. So an r2 of 89% tells us that more variation in y is explained than when r2 is 47%.
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