Join Monika Wahi for an in-depth discussion in this video Overview of modeling process, part of Healthcare Analytics: Regression in R.
- [Instructor] In this movie,…I'll give you an overview of the forward stepwise…modelling process we will use.…The first part of this movie goes into more detail…behind the actual steps in developing models one and two,…as I discussed earlier.…It also explains what I mean by model three being iterative,…and about developing a working final model,…and what that is.…Then I'll even explain to you what we have to do…to try to break our working final model.…Feeling destructive? Then you'll love that part.…
But before I get started, I wanted to remind you…of a slide I used earlier.…It explained our goal, which is to produce three models.…Model one was just the exposure in it.…Model two, which will be adjusted for age and sex,…and model three, the fully adjusted model.…Model three will be the result of our…forward stepwise modelling process.…So here's a big picture look at…the forward stepwise modelling process we will use.…We already discussed how we will start…by running model one with just the exposure variable in it.…
Author
Monika Wahi
Released
1/19/2017- Dealing with scientific plausibility
- Selecting a hypothesis
- Interpreting diagnostic plots
- Working with indexes and model metadata
- Working with quartiles and ranking
- Making a working model
- Improving model fit
- Performing linear regression modeling
- Performing logistic regression modeling
- Performing forward stepwise regression
- Estimating parameters
- Interpreting an odds ratio
- Adding odds ratios to models
- Comparing nested models
- Presenting and interpreting the final model
Skill Level Advanced
Duration
Views
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Introduction
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Introduction to the course1m 40s
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1. Designing Your Research
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Scientific method review5m 13s
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2. Preparing for Linear Regression
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Indexes7m 20s
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Quartiles3m 41s
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Ranking4m 45s
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Regression review3m 53s
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Preparing to report results2m 39s
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3. Beginning Linear Regression Modeling
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Overview of modeling process4m 58s
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Linear regression output5m 29s
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Models 1 and 23m 21s
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Model metadata4m 20s
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4. Final Linear Regression Modeling
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Beginning Model 35m 7s
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Making a working Model 35m 23s
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Finalizing Model 33m 33s
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Looking at the final model5m 58s
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Fishing and interaction5m 5s
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Defending the final model4m 27s
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Presenting the final model6m 27s
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5. Preparing for Logistic Regression
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Odds ratio interpretation6m 26s
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Basic logistic code3m 53s
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6. Developing the Logistic Regression Model
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Running Model 15m 24s
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Adding odds ratios to models5m 35s
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Model metadata6m 36s
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Forward stepwise: Round 25m 53s
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Models 1 and 2 presentation6m 30s
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Model 3 presentation4m 14s
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Conclusion
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Review of metadata4m 41s
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Review of the process3m 21s
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Next steps2m 19s
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Video: Overview of modeling process