class: center, middle, title-slide # Regression ## AU STAT-615 ### Emil Hvitfeldt ### 2021-1-20 --- # Welcome -- - Introductions - Syllabus - Material - Questions --- # Questions You are encouraged to ask questions when you have them rather than wait for me to ask for questions. If you have a question, chances are that something else has a question. --- # Attendence and Camera Both highly encouraged Due to COVID-19 both should be practiced according to what makes you safe --- # About Me - Data Analyst at Teladoc Health - R package developer, about 10 packages on CRAN (textrecipes, themis, paletteer, prismatic, textdata) - Co-author of "Supervised Machine Learning for Text Analysis in R" to be published soon --- ![](images/website.png) --- # Syllabus --- ## An Introduction to Statistical Learning with Applications in R Applied Linear Regression Models 4th edition or 5th edition You can either buy the 4th or the 5th edition, BUT DO NOT BUY BOTH --- ## Syllabus Come to me before it is too late I'm here to help, my main goal for this course is to make you succeed --- # Late assignment There are some (limited) late penalties It is more important for me that you turn something in then that you give up. You will always get points (sometimes reduced) for late assignments Contact me if you are having a hard time or need to turn in late --- # Lecture 40 + 15 + 40 + 15 + 40 --- # Labs A hands-on section where we work together on the implementation side in R These should be turned in WITH explanatory text. --- # Assignments There will be 10 assignments It Will contain a mix of conceptual/statistical questions and practical coding exercises about the weekly topic --- # Midterm We will have a midterm halfway through the course Will be a multi-day "take-home" test The Midterm will cover multiple weeks of material --- # Final Project The project will be a document(20%) and a presentation given to the class(5%) --- # Slack Discussion place and questions --- # Material 1/2 1. Introduction to R, notation, motivation, and examples. [1.1-1.2] 2. Linear regression: model, estimation, inference, prediction. Regression and correlation. R2. [Chap. 1-2] 3. Regression diagnostics: non-normality, nonlinearity, heteroscedasticity [Chap. 3] 4. Simultaneous estimation. Other regression models [Chap. 4] 5. Multiple regression. Matrix approach (Stat-615). Analysis of variance. Analysis of residuals. Partial correlation and multiple correlation coefficient. [Chap. 5-6] --- # Material 2/2 6. Model building. Model selection and validation. Extra sum of squares. [Chap. 7-8] 7. Regression diagnostics-II. Influential observations and outliers. Effect of multicollinearity. Robust regression. Ridge regression [Chap. 9-10] 8. Regression diagnostics-III. Symptoms and remedies. Transformation of variables. Missing data. Analysis of covariance. Comparison of regression lines. [Chap. 10] 9. Dummy variables and related methods [Chap. 11] 10. (If time permits) Nonlinear relations. Logistic regression. [Chap. 13-14]