Join Monika Wahi for an in-depth discussion in this video Indexes, part of Healthcare Analytics: Regression in R.
- [Instructor] Welcome to chapter three, section four,…where I continue teaching you strategies…of different things you can do…if your dependent continuous variable…is not meeting assumptions.…Another strategy is to make an index.…In the last section, I showed you…two potential fixes for continuous dependent variables…that do not meet assumptions,…and those are categorization and log transformation.…In categorization we essentially turned…a continuous variable into a categorical variable,…and in log transformation,…we just took a log of the variable.…
But now, I'm going to show you another alternative,…which is making an index.…In this case, you throw out the variable altogether…and make a brand new variable…out of other variables that approximate it.…Please note that my demonstration cannot focus…on using an index to create a sleep variable,…which is what we want as our continuous dependent variable.…That's because there are hardly any other questions…about sleep in the BRFSS core questions.…You need other questions on the same subject…
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: Indexes