17 OCT 2024 - Welcome Back to TorrentFunk! Get your pirate hat back out. Streaming is dying and torrents are the new trend. Account Registration works again and so do Torrent Uploads. We invite you all to start uploading torrents again!
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
VISITOR COMMENTS (0 )
FILE LIST
Filename
Size
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
