lecture 7 - binary logistic regression

Question 1

what type of variable is y when using OLS and linear regression models?

Answer 1

continuous

Question 2

when is binary logistic regression needed?

Answer 2

when the outcome variable is binary

Question 3

what does the binary logistic model predict?

Answer 3

the probability of getting y = 1

Question 4

what shape graph does the binary logistic model produce?

Answer 4

s shaped

Question 5

what are the key distinctions between OLS regression and binary logistic regression?

Answer 5

with OLS y can have any value

with BLR y can only fall between 0 and 1 as it predicts probability

Question 6

what are the steps in BLR including odds ratios

Answer 6

1. estimate probability for the case given Xs
2. convert the probability to odds (p/(1-p))
3. compare the odds of the 2 cases to get odds ratio

Question 7

how do you work out odds ratio?

Answer 7

odds after one unit increases in X / original odds

*the original odds is the category = 0

Question 8

odds ratio indicates a change in odds resulting from…?

Answer 8

a unit change in the predictor

Question 9

what do odds ratios centre on?

Answer 9

1

• > 1 = positive association
• <1 = negative association

Question 10

odds ratio is also referred to as?

Answer 10

exp (b)

Question 11

rather than obtaining odds ration you can?

Answer 11

convert odds to log odds which will make it linear

* not recommended as odds ratio are easier to interpret