Multiple Regression Flashcards

1
Q

What is r?

A

The multiple correlation coefficient (Persons)
- level of relationship between the model and the DV

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2
Q

What is r2?

A

Multiple coefficient of determination
- proportion of variance in Y accounted for by X

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3
Q

What is adjusted r2?

A

for general population

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4
Q

What is the standard error of the estimate?

A

the typical or average amount by which our predictions will be wrong
- 1-r2 = error variance, the % of variance in Y not explained by X

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5
Q

What is the major difference between correlational and experimental designs?

A

we cannot infer causation when we have not manipulated our IV (and potentially hold our confounding variables constant). If no experimental design cannot infer causation

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6
Q

What is the bivariate regression equation?

A

ÿ = a + (b)(X)

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7
Q

What do each of the symbols mean in the regression equation of ÿ = a + (b) (X)

A

ÿ = the predicted score on the Y variable
a = the y-intercept
b = the slope (B)
X = the persons score on the X variable

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8
Q

What is the z in the standardised regression equation?

A

z score

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9
Q

What is a residual score?

A

the difference between the actual score and the predicted score

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10
Q

What is the y-intercept or a?

A

the constant

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11
Q

What is the difference between the standardised and unstandarised regression equations?

A

The unstandardised uses B scores
The standardised uses Beta scores

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12
Q

What are beta weights?

A

are based on common metric and are comparable to each other
- give relative contribution of each predictor (IV) to the overall prediction of the DV

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13
Q

What is a zero order correlation?

A
  • the relationship between the DV and an IV irrespective of any other considerations
  • square to get the variance shared by these 2 variables
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14
Q

What is sr (Semi-partial correlation)?

A

The correlation between what’s left partioning out from X what it shares with Z (part correlation)
- square to get proportion of variance in the DV uniquely explained by the particular IV within a particular model (sr2)

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15
Q

How do you obtain a a sr2 (sr-squared) score?

A

square part correlation score (in coefficients SPSS output table)

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16
Q

What are the assumptions for multiple regression?

A

Normality
Linearity
Homoscedasticity
(no) multicollinearity

17
Q

What is the normality assumption for multiple regression?

A

residuals are normally distributed

18
Q

What is the linearity assumption for multiple regression?

A

the relationships between all variables (between IV’s and DV) are linear

19
Q

What is the homoscedasticity assumption for multiple regression?

A

the residuals are evenly spread out around the regression line

20
Q

What is the (no) multicollinearity assumption for multiple regression?

A

IV’s are not highly correlated with each other (do not want high correlation as regression coefficients will become unstable)