Standard Multiple Regression Flashcards

1
Q

What is multiple regression?

A

Variation as a function of multiple predictors

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2
Q

What is a strength of multiple regression?

A

Better prediction by accounting for more variance

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3
Q

What is multiple correlation?

A

R: Relationship between criterion and a set of predictors

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4
Q

What are the tests in multiple regression?

A
  1. Strength of overall relationship between the criterion and a set of predictors
  2. The importance of individual predictors
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5
Q

How is the strength of the overall relationship between the criterion and set of predictors measured?

A

R ^ 2

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6
Q

How is the importance of individual predictors measured?

A

β or sr

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7
Q

How is predictor importance indicated?

A

ry1^2 and ry2^2

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8
Q

What is R^2 with uncorrelated predictors?

A

ry1^2 + ry2^2

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9
Q

How is the correlation between predictor 1 and predictor 2 indicated?

A

r12^2

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10
Q

How is the variance in the DV accounted for by the shared variance between the predictors indicated?

A

ry12^2

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11
Q

Is R^2 smaller or larger than the sum of ry1^2 and ry2^2, and why?

A

Smaller, because it counts the shared variance twice

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12
Q

How is R^2 calculated with correlated predictors?

A

a + b + c
a: unique contribution of predictor 1
b: unique contribution of predictor 2
c: shared variance

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13
Q

What is partial correlation (pr2)?

A

The proportion of residual variance in the criterion uniquely accounted for by one predictor

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14
Q

How do you calculate partial correlation (pr2)?

A

pr2 = a / (a + d)
a: Unique contribution of predictor
d: Unexplained variance in the DV

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15
Q

What is semi-partial correlation (sr2)?

A

The proportion of total variance in the criterion uniquely accounted for by one predictor

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16
Q

How is semi-partial correlation calculated?

A

sr2 = a / (a + b + c + d)
a: Unique contribution of predictor 1
b: Unique contribution of predictor 2
c: Shared variance
d: Unexplained variance in the DV

17
Q

What is a + b + c + d?

A

The total variance in the DV

18
Q

What is the linear model in a standard multiple regression?

A

Yhat = b1X1 + b2X2 + a