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Applied Regression Analysis

Course Summary

Regression modeling is the standard method for analysis of continuous response data. This course provides theoretical and practical training in statistical modeling with particular emphasis on linear and multiple regression.


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    Course Syllabus

    Week One


    • Review of basic statistical concepts
    • Regression and correlation

    Week Two


    • Linear regression
    • Assumptions for linear regression
    • Hypothesis test and confidence intervals for model parameters

    Week Three


    • The correlation coefficient
    • The ANOVA table for straight line regression

    Week Four


    • Polynomial regression

    Week Five


    • Multiple regression
    • The partial F-test

    Week Six


    • Dummy (or indicator) variables
    • Statistical interaction
    • Comparing two straight line regression equations

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    Recommended Background

    Students interested in this course should have taken a solid introductory statistical methods course. While not absolutely necessary, familiarity with some statistical software package (such as SPSS, SAS or R) would definitely be an advantage. We will be making exclusive use of Stata for the course lectures and homework solutions. However, students who are proficient in other packages could use those packages in place of Stata. Based on past experience we strongly encourage students to use Stata for this course as it will facilitate communications with the professor, the teaching assistants, as well as other students in the course.

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    Course Format

    The class will consist of lecture videos, quiz questions and homework exercises.

    The homework exercises are ungraded and students are expected to complete them using the statistical package Stata (Available to all course students during the six week period of this course). 

    Solutions to all homework problems will be provided to help students with the necessary Stata commands. (This process will be carefully explained in the course)
    Discussion boards will be used in this class for conversation with your peers, the faculty and the course team.
    The course slides will be provided to all students. If printed, these slides would serve as the course textbook and could serve as a useful reference for the future.

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    Suggested Reading

    All core materials for this course will be provided within the course at no cost to the student.

    Although this course is designed to be self-contained, you may supplement your learning experience in this course by purchasing this optional and excellent textbook – Applied Regression Analysis and Multivariable Methods (4th Edition) by Kleinbaum, Kupper, Muller, and Nizam


     


Course Fee:
Free

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

This course is listed under Development & Implementations and Data & Information Management Community

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