Healthcare Analytics Regression in R

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Size 519.37 Mb
Seed 2
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Hash 7FDBFD8D5E9BF10E353587F2383918F0C6C841FC
Date June 13, 2017 at 7:24:00 PM PDT

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  • Healthcare Analytics Regression in R
    • 00_01 - Welcome to the course.mp4 (6.35 Mb)
    • 00_02 - What you should know.mp4 (1.66 Mb)
    • 00_03 - Introduction to the course.mp4 (2.56 Mb)
    • 00_04 - Using the exercise files.mp4 (1.12 Mb)
    • 01_01 - Scientific method review.mp4 (11.87 Mb)
    • 01_02 - Using a cross-sectional approach.mp4 (11.60 Mb)
    • 01_03 - Reviewing existing literature for ideas.mp4 (12.91 Mb)
    • 01_04 - Dealing with scientific plausibility.mp4 (11.22 Mb)
    • 01_05 - Selecting a linear regression hypothesis.mp4 (13.33 Mb)
    • 01_06 - Selecting a logistic regression hypothesis.mp4 (17.66 Mb)
    • 01_07 - Installing necessary packages.mp4 (10.28 Mb)
    • 02_01 - Plots for checking assumptions in linear regression.mp4 (10.92 Mb)
    • 02_02 - Interpreting diagnostic plots.mp4 (5.09 Mb)
    • 02_03 - Categorization and transformation.mp4 (11.85 Mb)
    • 02_04 - Indexes.mp4 (14.73 Mb)
    • 02_05 - Quartiles.mp4 (6.62 Mb)
    • 02_06 - Ranking.mp4 (8.12 Mb)
    • 02_07 - Regression review.mp4 (7.00 Mb)
    • 02_08 - Preparing to report results.mp4 (4.46 Mb)
    • 03_01 - Choices of modeling approaches.mp4 (9.02 Mb)
    • 03_02 - Overview of modeling process.mp4 (8.76 Mb)
    • 03_03 - Linear regression output.mp4 (10.13 Mb)
    • 03_04 - Models 1 and 2.mp4 (7.04 Mb)
    • 03_05 - Model metadata.mp4 (7.48 Mb)
    • 04_01 - Beginning Model 3.mp4 (13.70 Mb)
    • 04_02 - Making a working Model 3.mp4 (16.91 Mb)
    • 04_03 - Finalizing Model 3.mp4 (11.79 Mb)
    • 04_04 - Looking at the final model.mp4 (14.63 Mb)
    • 04_05 - Fishing and interaction.mp4 (9.81 Mb)
    • 04_06 - Other strategies for improving model fit.mp4 (5.87 Mb)
    • 04_07 - Defending the final model.mp4 (7.52 Mb)
    • 04_08 - Presenting the final model.mp4 (17.22 Mb)
    • 05_01 - Analogies to linear regression process.mp4 (8.63 Mb)
    • 05_02 - Parameter estimates in logistic regression.mp4 (7.68 Mb)
    • 05_03 - Odds ratio interpretation.mp4 (10.23 Mb)
    • 05_04 - Basic logistic code.mp4 (6.47 Mb)
    • 05_05 - Forward stepwise regression First two rounds.mp4 (7.22 Mb)
    • 05_06 - Forward stepwise regression Round 3.mp4 (11.48 Mb)
    • 06_01 - Running Model 1.mp4 (10.64 Mb)
    • 06_02 - Adding odds ratios to models.mp4 (12.02 Mb)
    • 06_03 - Model metadata.mp4 (12.30 Mb)
    • 06_04 - Forward stepwise Round 2.mp4 (13.07 Mb)
    • 06_05 - Forward stepwise Round 3.mp4 (18.49 Mb)
    • 06_06 - Using AIC to assess model fit.mp4 (7.63 Mb)
    • 06_07 - When to compare nested models.mp4 (7.69 Mb)
    • 06_08 - How to compare nested models.mp4 (16.10 Mb)
    • 06_09 - Models 1 and 2 presentation.mp4 (15.05 Mb)
    • 06_10 - Model 3 presentation.mp4 (13.72 Mb)
    • 06_11 - Interpreting the final model.mp4 (11.07 Mb)
    • 07_01 - Review of metadata.mp4 (8.67 Mb)
    • 07_02 - Review of the process.mp4 (6.20 Mb)
    • 07_03 - Next steps.mp4 (4.11 Mb)
    • Ex_Files_Healthcare_Regression_R.zip (1.61 Mb)
    • Subtitles CC.rar (79.03 Kb)
    • Torrent Downloaded From Katcr.co - Kickasstorrents.txt (0.05 Kb)

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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