Meng Li

STAT 410: Linear Regression

 

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Textbook

Montgomery, Peck and Vining, Introduction to Linear Regression Analysis, 5th ed, ISBN-10: 0470542810

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Course description

STAT 410 is an introductory course to regression analysis with an emphasis on both statistical inferences and applications through the use of the statistical software R. No previous knowledge of computer programming is required. Regression analysis is one of the most widely used statistical methods to analyze data across a multitude of disciplines. Many investigators want to know how certain “explanatory” variables might be associated with a response. The simplest case involves one explanatory variable (x) and one response (y). How is income (y) associated with education (x)? Are cholesterol levels (y) associated with age (x)? Is mental response time (y) associated with alcohol consumption (x)? More complex studies consider many, sometimes even thousands of explanatory variables. Indeed, is asthma associated with one or some of the 30,000 genes found in our DNA? Regression provides a modeling framework for these types of questions and data. The course will introduce students to concepts of regression, regression models, estimation methods to fit regression models to data, regression model development, inferential methods for regression model parameters, and diagnostic measures to assess how well our regression modeling squares with the data. Through data based assignments students will also learn how to run regression analyses and report statistical results.

Prerequisites: STAT 310 or equivalent. A linear algebra course such as CAAM 335 or Math 355 is recommended.

All class materials are distributed on-line; for example, you may view class notes and homework assignments on Piazza. Canvas is used to report scores from homeworks, labs, and examinations.

A sample list of topics: overview of statistical inference, correlation, normal equations, estimation of parameters, maximum likelihood estimators, ANOVA, the coefficient of determination, model diagnostics, t-test, F-test, multicollinearity, prediction, interaction, model selection.

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Grades

The course grade will consist of homework (20%), labs (5%), projects (25%), Exam 1 (25%; Wednesday, Feb 22), and Exam 2 (25%; Wednesday, April 19). All grades are reported on Canvas. For homework, late submissions are penalized (see below), and missed submissions receive zero scores, but each student’s lowest homework score is dropped.

Homework Assignments

The homework assignments will consist of theory, methodology, and data problems. The purpose of the data problems is to facilitate learning R commands and code for various aspects of regression analysis. Homework will be assigned during the semester on a regular basis. All assignments are submitted on Canvas.

Labs

The lab is an important component of this course. We shall use labs to introduce the software R, review linear algebra, cover more examples and homework, and address other selected aspects of regression analysis.

Projects

The projects will focus on real data sets with substantive questions and/or advanced topics in linear regression, submitted as formal statistical reports. The allotted time for a project is usually two weeks. More information including the guidelines will be discussed in class.

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Policy on Submitted Work

Students are permitted to work together on all assignments unless otherwise noted. However, each student is expected to submit their own individual work based on their own individual expression. Papers that show an overabundance of similarity will receive a grade of zero. The interpretation of “an overabundance of similarity” is left to the instructor’s discretion. If you have any doubt, then you are encouraged to discuss the matter with the instructor before your work is due.

If circumstances beyond a student’s control arise and an assignment cannot be submitted on the due date, the instructor should be contacted prior to the due date. Late homework will be accepted within three days of the due date with a 10-percentage point deduction per day (including weekends). Thus, if you are late 3 days, then you will receive a 30-percentage point deduction. All decisions regarding late homework will be made on an individual student basis and the final decision rests with the instructor.

 

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Email & Forum (Piazza)

I will send announcements by email, please make sure to check your email daily. I will try and make all announcements in class but ultimately you are responsible for all course information given in class and through this course website and emails.

Any non-personal questions related to the material covered in class, problem sets, labs, projects, etc. should be posted on Piazza. Before posting a new question please make sure to check if your question has already been answered. The TAs and I will be answering questions on the forum and all students are expected to answer questions as well. Please use informative titles for your posts.

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Disability Support Services

If you have a documented disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / adarice@rice.edu / x5841) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.

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Missed Work and Absences

No excuse is needed simply for missing class, for whatever reason, only for missed assignments and examinations. Class attendance isn’t required, but exams will cover material presented in class that may not be covered in the text or homework assignments.

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Rice Honor Code

In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook at http://honor.rice.edu/honor-system-handbook/. This handbook outlines the University’s expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process.

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