2.2

Question 1

Assessing Predictors: The Wald Statistic

Answer 1

• Similar to t-statistic in regression.
• Tests the null hypothesis that 𝑏 = 0.
• Is biased when 𝑏 is large.
• Better to look at Odds Ratio statistics.

Question 2

𝑜𝑑𝑑𝑠 𝑟𝑎𝑡𝑖𝑜 = 3.42 example meaning

Answer 2

Patients that get the intervention are 3.42 times more likely to get cured than patients without the intervention.

Question 3

odds ratio

Answer 3

odds after a unit change in the predictor / odds before a unit change in the predictor

the ratio of your chances of getting cured when taking the medication / the chances of getting cured without taking the medication
-> that is actually the effect of the treatment that you want to investigate

Question 4

high / low: log − likelihood

Answer 4

The higher the value, the better a model fits a dataset.

log-likelihood isa measure the goodness of fit for a model

Question 5

Model deviance

Answer 5

• It is an indicator of how much unexplained information there is after the model has been fitted.
• Large values indicate poorly fitting statistical models. When our model is precise, the LL will be close to 0.

deviance = -2LL

Question 6

Things That Can Go Wrong

Answer 6

Unique problems
• Incomplete information
• Complete separation

Question 7

Incomplete information from the predictors

Answer 7

Categorical predictors:
• Predicting cancer from smoking and eating tomatoes.
• We don’t know what happens when non-smokers eat tomatoes because we have no data in this cell of the design.

Question 8

Complete Separation

Answer 8

• When the outcome variable can be perfectly predicted by an X variable, you cannot run the model because there is complete separation in the data.
• If you think about it: This might actually not be a problem, because if X indeed perfectly predicts Y, this might be very helpful to you.