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STAT 425

Fall 2021

Statistical Modeling I

Course Description

– This course will mainly be focused on statistical analysis techniques using regression mod-els. Starting from linear regression, this course will cover statistical inference for regression, regression diagnostics and remedial measures, transformations, model building and variable selection, as well as extensions such as polynomial and non-parametric regression, ridge and lasso regression.

– The course will also discuss the principles of experimental design, randomization and permutation tests, and will introduce ANOVA models (with fixed, random and mixed effects), generalized estimating equations and longitudinal data.

Course Objectives

– Understand the principles, mathematical models and methods for estimation and inference on linear models and extensions.

– Interpret models results and model diagnostics and make necessary model adjustments.

– Utilize R for the purpose of analyzing statistical data.

– Learn to communicate the results of a statistical analysis through oral and written presentations.

Textbook & References

1. Linear Models with R (2nd edition) by Julian Faraway. An earlier version of this book, Practical Regression and Anova Using R, as well as other related documentations, can be downloaded here.

2. Extending the linear model with R (2nd edition) by Julian Faraway.

3. Applied Linear Regression (4th Edition) by Sanford Weisberg. An earlier version can be downloaded from our Library.

4. A Modern Approach to Regression with R, by Simon J. Sheather. You can download this book (pdf) from our Library.

5. Applied Linear Statistical Models, McGraw-Hill, by M. H. Kutner, C. J. Nachtsheim, J. Neter, W. Li

6. An introduction to Statistical Learning by G. James, D. Witten,T. Hastie and R. Tibishirani.

Coursework

– Homework Assignments

There will be 8 – 9 homework assignments throughout the semester. All homework assign-ments will be submitted online on Gradescope. No late homework will be accepted. The homework will account for 25% of the course grade.

– Lecture Quizzes

There will be a short lecture quiz corresponding to each lecture. They will be submitted in Gradescope and will account for 5% of the course grade. The 3 lowest quiz scores will be dropped.

– Case Studies

3-credit students: There will be 1 case study that will account for 5% of the course grade.

4-credit students: There will be 2 case studies that will account for 10% of the course grade.

– Exams

There will be 2 midterms, each one accounting for 25% of the course grade.

– Project

There will be a final project instead of a final exam. The project will account for 10% of the course grade for 4 credit students and 15% of the course grade for 3-credit students .

Course Software

In the assignments, case studies, and project, you are required to use R.

Grading

Re-grading

If you want to dispute your work’s grade, all requests should be made via Gradescope or by email to the instructor within a week after receiving your graded work. Please note that when you ask for a question to be re-graded, the entire assignment may be re-graded.

Academic Integrity

It is expected that all students will support the idea of academic integrity and be responsible for the integrity of their work. The university has a published policy on academic integrity that may be found at http://www.library.illinois.edu/learn/research/academicintegrity.html