Lynda - Healthcare Analytics: Regression in R
Lynda - Healthcare Analytics: Regression in R
Size: 520 MB | Duration: 4h 2m | Video: AVC (.mp4) 1280x720 30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Advanced | Language: English
Size: 520 MB | Duration: 4h 2m | Video: AVC (.mp4) 1280x720 30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Advanced | Language: English
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.
* 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
* 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
Lynda - Healthcare Analytics: Regression in R
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