L6 - Regression Analysis III Flashcards

1
Q

What do the Aikake information criteria (AIC) and the Bayesian information criteria (BIC) take into account?

A

Model complexity.

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2
Q

Are lower or higher values for AIC and BIC better?

A

lower values are better.

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3
Q

What do you use if the DV is categorical/binary?

A

logistic regression

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4
Q

How does the logistic function look like?

A
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5
Q

how does the regression equation look like in logistics regression?

A
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6
Q

Does the slope in the regression equation in logistics regression stand for the change in log odds associated with a one-unit change in X?

A

Yes

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7
Q

How does the intercept change when you have a smaller value of ß0?

A

It moves to the right

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8
Q

How does the slope change when you have a smaller value of ß1?

A

for negative values it is downwards sloping

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9
Q

How to get probability from ods?

A

odds/ (1 + odds)

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10
Q

what are the log odds of odds of 1:1?

A

log (1/1) = 0

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11
Q

how to get odds from log odds?

A

exponeniate

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12
Q

What does the .357 tell you?

A
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13
Q

If you have a weight of 70 then was is the probability of being male? The odds are .52

A

You just have to put odds/ (1+ odds)

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14
Q

p(survival) female, 40years, 2nd class?

A
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15
Q

What is the Wald statistic used for?

A

To see if a regression weight is significantly different from zero.

  • you need the regression coefficient and standard error of the coefficient
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16
Q

What is the log likelihood used for (LL)?

A
  • about the likelihood of the data we have observed
  • we want to LL to be as high as possible
17
Q

What does Chi-squared measure? model fit.

A

(deviance baseline (only intercept)) - (deviance model)

-> deviance should be as small as possible
–> high X^2 means that model is way better than baseline

18
Q

What is Cox & Snell R squared?

A
  • ranges from 0 to 1
  • 1 is the best
  • to measure model fit
19
Q

What is the Nagelkerke R-squared?

A
  • larger R^2 than Cox and Snell
  • to measure model fit
20
Q

Why are AIC and BIC better than R^2?

A

Because a high fit is not always good -> AIC and BIC let you evaluate complexity vs. model fit

21
Q

What is a special assumtion for logistics regression?

A

no complete separation

–> logistics regression does not know how to handle a gap in between. It is better if there is an overlap

22
Q

How many events should you have per predictor in logistics regression

A

min 10
(events are the cases in the less frequent category of the DV)