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The slope of a regression tells us: The covariance of X and Y The marginal impact...

The slope of a regression tells us:

  1. The covariance of X and Y
  2. The marginal impact of X on Y
  3. The marginal impact of Y on X
  4. The level of X when Y is zero
  5. The level of Y when X is zero

2. The intercept of a regression tells us:

  1. The level of Y when X is zero
  2. The level of X when Y is zero
  3. The marginal impact of Y on X
  4. The marginal impact of X on Y
  5. The covariance of X and Y

3. ∑(Y – Ŷ)² is essentially a measure of

  1. How much our predictions miss the actual data
  2. How much variance we explain with X
  3. How much variance we explain with Y
  4. The covariance between the prediction and X
  5. How much our predictions deviate from X

4. The main difference between the calculation of Pearson’s r and the slope of a regression is

  1. The inclusion of the covariance in the numerator
  2. The inclusion of the SSx in the denominator
  3. The inclusion of the SSy in the denominator
  4. The inclusion of the SSx in the numerator
  5. The inclusion of the SSy in the numerator

5. A regression with a slope of 4 tells us

  1. The slope is large and significant
  2. The slope is large but not significant
  3. The slope is small and significant
  4. The slope is small and not significant
  5. Not enough information to decide

6. A significance test for beta that fails to reject the null

  1. Cannot distinguish beta from zero
  2. Lacks sufficient information to make a decision
  3. Can distinguish beta from zero
  4. Tells us that beta is negative
  5. Tells us we have made a Type I Error

The slope of a regression tells us:

  1. The covariance of X and Y
  2. The marginal impact of X on Y
  3. The marginal impact of Y on X
  4. The level of X when Y is zero
  5. The level of Y when X is zero

2. The intercept of a regression tells us:

  1. The level of Y when X is zero
  2. The level of X when Y is zero
  3. The marginal impact of Y on X
  4. The marginal impact of X on Y
  5. The covariance of X and Y

3. ∑(Y – Ŷ)² is essentially a measure of

  1. How much our predictions miss the actual data
  2. How much variance we explain with X
  3. How much variance we explain with Y
  4. The covariance between the prediction and X
  5. How much our predictions deviate from X

4. The main difference between the calculation of Pearson’s r and the slope of a regression is

  1. The inclusion of the covariance in the numerator
  2. The inclusion of the SSx in the denominator
  3. The inclusion of the SSy in the denominator
  4. The inclusion of the SSx in the numerator
  5. The inclusion of the SSy in the numerator

5. A regression with a slope of 4 tells us

  1. The slope is large and significant
  2. The slope is large but not significant
  3. The slope is small and significant
  4. The slope is small and not significant
  5. Not enough information to decide

6. A significance test for beta that fails to reject the null

  1. Cannot distinguish beta from zero
  2. Lacks sufficient information to make a decision
  3. Can distinguish beta from zero
  4. Tells us that beta is negative
  5. Tells us we have made a Type I Error

Solutions

Expert Solution

milcah answered 6 months ago
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