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MA251: Statistics II

Unit 4: Generalized Linear Model and Nonlinear Regression   In this unit, you will be introducd to several new regression models, including logistic regression, polynomial regression, and exponential regression. These statistical models will provide you with a set of toolboxes to analyze a wide range of problems. For instance, logistic regression enables you to conduct regression analysis to predict outcome of a binary dependent variable (e.g., “success” vs. “failure”). This model is often used in medicine to predict risk of developing disease in a predefined period. You also will learn about generalized linear models, which were formulated as a way to unify various statistical models, including linear regression and logistic regression.

Unit 4 Time Advisory
This unit will take you approximately 25 hours to complete.

☐    Subunit 4.1: 3 hours

☐    Subunit 4.2: 4 hours

☐    Subunit 4.3: 3 hours

☐    Subunit 4.4: 15 hours

Unit4 Learning Outcomes
Upon successful completion of this unit, the student will be able to:

  • explain the rationale behind Generalized Linear Models;
  • perform polynomial regression;
  • perform logistic regression; and
  • perform exponential regression.

4.1 Generalized Linear Models   - Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 14: Model Building with Multiple Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

[Submit Materials](/contribute/)

4.2 Nonlinearity: Polynomial Regression   - Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 14: Model Building with Multiple Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

[Submit Materials](/contribute/)

4.3 Exponential Regression and Log Transforms   - Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 14: Model Building with Multiple Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

[Submit Materials](/contribute/)

4.4 Logistic Regression   4.4.1 Logistic Regression Basics   - Lecture: Medical College of Wisconsin: Sergey Tarima’s “Logistical Regression” Link: Medical College of Wisconsin: Sergey Tarima’s “Logistical Regression (YouTube and PDF)

 Instructions: Click on the link for the video for the lecture on
“Logistic Regression,” dated June 24, 2010 and watch the video. You
may also want to download the presentation in PDF format. In this
lecture, Sergey Tarima discusses logistic regression, using medical
examples. You will learn about odds, odds ratio, and how to
interpret results of a simple logistic regression model.  

 Watching this lecture and pausing to take notes should take
approximately 1 hour and 15 minutes.  

 Terms of Use: Please respect the copyright and terms of use
displayed on the webpage above.
  • Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 15: Logistic Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

    Submit Materials

4.4.2 Multiple Logistic Regression   - Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 15: Logistic Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

[Submit Materials](/contribute/)

4.4.3 Inference for Logistic Regression Models   - Reading: University of Florida: Alan Agresti’s Statistical Methods for the Social Sciences II: “Chapter 15: Logistic Regression” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

[Submit Materials](/contribute/)
  • Assessment: Carnegie Mellon University: Cosma Shalizi’s “Advanced Data Analysis: Homework 7” Link: CarnegieMellon University: Cosma Shalizi’s “Advanced Data Analysis: Homework 7 (PDF)

    Instructions: Click on the link above and download the PDF file for Homework 7 (hw-07.pdf). Follow the instructions for the problems closely. The solutions to the homework are in the PDF file, solutions-07.pdf. 

    Completing this assignment should take approximately 5 hours. 

    Terms of Use: Please respect the copyright and terms of use displayed on the webpage above.

  • Assessment: The Saylor Foundation’s “Logistic Regression” Link: The Saylor Foundation’s “Logistic Regression (PDF)
     
    Instructions: Complete the linked assessment. When you are done, check your work against The Saylor Foundation’s “Answer Key (PDF) for subunit 4.4 in R.

    If you have not done so already, click on the following link to download and install R on your computer: http://cran.r-project.org/. R will be used throughout the course for assignments.

    Completing this assessment should take you no longer than 3 hours.

Unit 4 Assessment   - Assessment: The Saylor Foundation’s “Unit 4 Assessment” Link: The Saylor Foundation’s “Unit 4 Assessment”
 
Instructions: Complete this assessment to gauge your understanding of the materials covered thus far in this course. When you click “submit,” you will be shown the correct answers.