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Healthcare Analytics Regression in R

Size
519 MB
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3
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0
Files
54
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at 2:38am GMT+1
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Description
Linear and logistic regression models can be created using R, the open-source statistical computing software. In this course, biotech expert and epidemiologist Monika Wahi uses the publicly available Behavioral Risk Factor Surveillance Survey (BRFSS) dataset to show you how to perform a forward stepwise modeling process. Monika shows you how to design your research by considering scientific plausibility selecting a hypothesis. Then, she takes you through the steps of preparing, developing, and finalizing both a linear regression model and a logistic regression model. She also shares techniques for how to interpret diagnostic plots, improve model fit, compare models, and more.

Topics include:


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

File list
  • Healthcare Analytics Regression in R
  • 00_01 - Welcome to the course.mp4 6.3 MB
  • 00_02 - What you should know.mp4 1.7 MB
  • 00_03 - Introduction to the course.mp4 2.6 MB
  • 00_04 - Using the exercise files.mp4 1.1 MB
  • 01_01 - Scientific method review.mp4 11.9 MB
  • 01_02 - Using a cross-sectional approach.mp4 11.6 MB
  • 01_03 - Reviewing existing literature for ideas.mp4 12.9 MB
  • 01_04 - Dealing with scientific plausibility.mp4 11.2 MB
  • 01_05 - Selecting a linear regression hypothesis.mp4 13.3 MB
  • 01_06 - Selecting a logistic regression hypothesis.mp4 17.7 MB
  • 01_07 - Installing necessary packages.mp4 10.3 MB
  • 02_01 - Plots for checking assumptions in linear regression.mp4 10.9 MB
  • 02_02 - Interpreting diagnostic plots.mp4 5.1 MB
  • 02_03 - Categorization and transformation.mp4 11.9 MB
  • 02_04 - Indexes.mp4 14.7 MB
  • 02_05 - Quartiles.mp4 6.6 MB
  • 02_06 - Ranking.mp4 8.1 MB
  • 02_07 - Regression review.mp4 7 MB
  • 02_08 - Preparing to report results.mp4 4.5 MB
  • 03_01 - Choices of modeling approaches.mp4 9 MB
  • 03_02 - Overview of modeling process.mp4 8.8 MB
  • 03_03 - Linear regression output.mp4 10.1 MB
  • 03_04 - Models 1 and 2.mp4 7 MB
  • 03_05 - Model metadata.mp4 7.5 MB
  • 04_01 - Beginning Model 3.mp4 13.7 MB
  • 04_02 - Making a working Model 3.mp4 16.9 MB
  • 04_03 - Finalizing Model 3.mp4 11.8 MB
  • 04_04 - Looking at the final model.mp4 14.6 MB
  • 04_05 - Fishing and interaction.mp4 9.8 MB
  • 04_06 - Other strategies for improving model fit.mp4 5.9 MB
  • 04_07 - Defending the final model.mp4 7.5 MB
  • 04_08 - Presenting the final model.mp4 17.2 MB
  • 05_01 - Analogies to linear regression process.mp4 8.6 MB
  • 05_02 - Parameter estimates in logistic regression.mp4 7.7 MB
  • 05_03 - Odds ratio interpretation.mp4 10.2 MB
  • 05_04 - Basic logistic code.mp4 6.5 MB
  • 05_05 - Forward stepwise regression First two rounds.mp4 7.2 MB
  • 05_06 - Forward stepwise regression Round 3.mp4 11.5 MB
  • 06_01 - Running Model 1.mp4 10.6 MB
  • 06_02 - Adding odds ratios to models.mp4 12 MB
  • 06_03 - Model metadata.mp4 12.3 MB
  • 06_04 - Forward stepwise Round 2.mp4 13.1 MB
  • 06_05 - Forward stepwise Round 3.mp4 18.5 MB
  • 06_06 - Using AIC to assess model fit.mp4 7.6 MB
  • 06_07 - When to compare nested models.mp4 7.7 MB
  • 06_08 - How to compare nested models.mp4 16.1 MB
  • 06_09 - Models 1 and 2 presentation.mp4 15 MB
  • 06_10 - Model 3 presentation.mp4 13.7 MB
  • 06_11 - Interpreting the final model.mp4 11.1 MB
  • 07_01 - Review of metadata.mp4 8.7 MB
  • 07_02 - Review of the process.mp4 6.2 MB
  • 07_03 - Next steps.mp4 4.1 MB
  • Ex_Files_Healthcare_Regression_R.zip 1.6 MB
  • Subtitles CC.rar 79 KB

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