2.2 Flashcards

1
Q

Assessing Predictors: The Wald Statistic

A
  • 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.
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2
Q

π‘œπ‘‘π‘‘π‘  π‘Ÿπ‘Žπ‘‘π‘–π‘œ = 3.42 example meaning

A

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

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3
Q

odds ratio

A

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

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4
Q

high / low: log βˆ’ likelihood

A

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

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

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5
Q

Model deviance

A
  • 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

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6
Q

Things That Can Go Wrong

A

Unique problems
β€’ Incomplete information
β€’ Complete separation

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7
Q

Incomplete information from the predictors

A

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

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8
Q

Complete Separation

A
  • 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.
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