4. Multiple linear regression Flashcards

1
Q

What are the 2 goals of a MLR?

A

get b values

reduce error

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2
Q

What are the 3 types of correlation?

A

Partial
Semi-Partial
Multicollinearity

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3
Q

What are the 2 reasons that multicollinearity is a problem?

A

It reduces the size of R

It obscures the importance of individual predictors

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4
Q

What type of multiple linear regression could you use if multicollinearity is present but you would like to keep the predictors in the model because they explain the underlying construct?

A

Stepwise

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5
Q

What type of MLR has all IVs entered into analysis at once?

A

Simultaneous

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6
Q

How does a simultaneous MLR work?

A

It assesses each variable as if it had been entered into the regression after all the others

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7
Q

What should you generally enter in first in a simultaneous MLR?

A

The highest correlated (biggest r)

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8
Q

How are variables entered into a Hierarchical MLR?

A

Based on some external criterion of our own, like a theoretical model

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9
Q

What are the different types of MLR?

A

Simultaneous
Hierarchical
Step-wise (forward, backward)

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10
Q

In a Step-wise MLR how are variables entered?

A

Only those variables that contribute statistically to the prediction of the DV

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11
Q

When should a step-wise MLR not be used?

A

Sample less than 40

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12
Q

What does a backwards Step-wise MLR do?

A

Enters everything and then removes those that aren’t sig. contributing

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13
Q

What does a forward Step-wise MLR do?

A

Enters IVs with strongest r first and others afterwards only if they can explain sig. further variance

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14
Q

Why is backwards step-wise MLR better than forwards?

A

Forwards = suppressor effect (greater possibility of making Type II error)

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