Learn how to use the covariates removed earlier in the modeling process to break the working model to ensure robustness.
- [Teacher] Welcome to this section…where we do round two of SS modeling…and have a little fun breaking our working model.…But for now, let's get down to business.…In round two, you start with the working model…you developed in round one where you tried all…the covariates and only kept the ones that were significant,…or almost significant in the model,…along with the exposure variable.…So basically, your working model has a bunch…of covariates in it and most, if not all of them,…are statistically significant.…Remember table one, your descriptive table?…You listed all of your potential covariates in that.…
So I use table one as a guide as to what order to go in,…when putting covariates in the model,…both in round one and round two.…So go pick out the first discarded variable…that's not in your working model.…Go ahead and add that one to the model and run it,…then look at it and update your metadata.…And under the common column, state your verdict.…Keep it, or toss it.…Should it stay, or should it go?…Alright, don't get me singing The Clash.…
- Differentiate between modular code and spaghetti code and explain when to use each.
- Explain the data-set transformation approach.
- Assess the right time to remove identifiers from the data set.
- Cite the considerations for categorical outcomes.
- Recognize that with large data, even small differences are statistically significant.
- Determine when using a stepwise model is appropriate.