Join Monika Wahi for an in-depth discussion in this video Review of metadata, part of Healthcare Analytics: Regression in R.
- [Instructor] Wow!…Can you believe it?…We have made it to the last chapter, chapter seven.…Great job conducting your analysis.…But before we part, let's review the metadata…we accrued during this analysis.…So, in this movie, I'm just going to review…all the metadata we made during this course.…I'm going to remind you of what we made…and how we made it, and also how it was used…during our analysis.…I'm also going to tell you…how you can keep using it going forward.…I just want to remind you of the metadata you made…in the prerequisite course on descriptive analysis.…
That's because we used some of it in this course.…We used the data dictionary, listed under one,…and the descriptive presentation table, listed under four,…to help us when we were modeling.…We also used the code book from BRFSS,…which would fall under 6, miscellaneous.…The other documentation is more helpful to us…when writing up the analysis.…In this course, we made some important metadata as well.…Our comments and our code files, especially during modeling,…
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: Review of metadata