## 36-401 Modern Regression

Instructor:Larry Wasserman

Time:Tuesday and Thursday 12:00 - 1:20

Place:PH 100

Office Hour:Tuesdays 1:30 - 2:30 Baker Hall 132G

## TA Information

TA:Collin Eubanks (Head TA)

Email:ceubanks@andrew.cmu.edu

Office Hours:Thursdays 1:30 - 2:30 BH 132Q

TA:Riccardo Fogliato

Email:rfogliat@andrew.cmu.edu

Office Hours:Wednesdays 3:30 - 4:30 BH 132A

TA:Boyan Duan

Email:boyand@andrew.cmu.edu

Office Hours:Thursdays 10:30 - 11:30 Wean Hall 4625

TA:Xiaoyi Gu

Email:xgu1@andrew.cmu.edu

Office Hours:Friday 10:00-11:00 BH132Q

TA:Jining Qin

Email:jiningq@andrew.cmu.edu

Office Hours:Thursdays 4:00-5:00 BH 132Q

Course Assistant:Mari-Alice McShane mcshane@stat.cmu.edu

Office:Baker Hall 229K

## Syllabus

Click here for syllabus

## Course Description

This course is an introduction to applied data analysis. We will explore data sets, examine various models for the data, assess the validity of their assumptions, and determine which conclusions we can make (if any). Data analysis is a bit of an art; there may be several valid approaches. We will strongly emphasize the importance of critical thinking about the data and the question of interest. Our overall goal is to use a basic set of modeling tools to explore and analyze data and to present the results in a scientific report. The course includes a review and discussion of exploratory methods, informal techniques for summarizing and viewing data. We then consider simple linear regression, a model that uses only one predictor. After briefly reviewing some linear algebra, we turn to multiple linear regression, a model that uses multiple variables to predict the response of interest. For all models, we will examine the underlying assumptions. More specifically, do the data support the assumptions? Do they contradict them? What are the consequences for inference? Finally, we will explore extra topics such as nonlinear regression or regression with time-dependent data. A minimum grade of C in any one of the pre-requisites is required. A grade of C is required to move on to 36-402 or any 36-46x course. Prerequisites: At least a C grade in (36-226 or 36-625 or 73-407 or 36-310) and (21-240 or 21-241).

Textbook:Applied Linear Regression Models, Fourth Edition by Kutner, Nachtsheim and Neter.

R Stuff

An R Tutorial

data for R Tutorial

R reference card

A thorough R tutorial

## Prerequisites

Prerequisites: At least a C grade in (36-226 or 36-625 or 73-407 or 36-310) and (21-240 or 21-241).

Data Analysis Project 1 due Friday October 13 by 3:00. Upload it using Canvas.

Data Analysis Project 2 due Tues Nov 21 at 5:00. Upload it on Canvas.

## Assignments

Assignments are due onFridays at 3:00 p.m.Upload your assignment in Canvas.

No late assignments will be accepted. If you need an extension due to illness, email me BEFORE the deadline.

Homework 1 due Friday September 8 by 3:00. Upload it using Canvas.

Homework 2 due Friday September 15 by 3:00. Upload it using Canvas.

Homework 3 due Friday September 22 by 3:00. Upload it using Canvas.

Homework 4 due Friday September 29 by 3:00. Upload it using Canvas.

Homework 5 due Friday October 20 by 3:00. Upload it using Canvas.

Homework 6 due Friday October 27 by 3:00. Upload it using Canvas.

Homework 7 due Friday November 3 by 3:00. Upload it using Canvas.

Homework 8 due Friday November 10 by 3:00. Upload it using Canvas.

Homework 9 due Friday December 1 by 3:00. Upload it using Canvas.

Homework 10 due Friday December 8 by 3:00. Upload it using Canvas.

## Solutions

Homework 1 Solutions

Homework 2 Solutions

Homework 3 Solutions

Homework 4 Solutions

Test 1

Test 1 Solutions

Homework 5 Solutions

Homework 6 Solutions

Homework 7 Solutions

Homework 8 Solutions

Homework 9 Solutions

## Lecture Notes (Written by Professor Cosma Shalizi)

Download the notes and bring them to class.

Lecture Notes 1

Lecture 2 was an R tutorial

Lecture Notes 3

Lecture Notes 4

Lecture Notes 5

Lecture Notes 6

Lecture Notes 7

Lecture Notes 8

Lecture Notes 9

Lecture Notes 10

Lecture 11 was review

Lecture 12 was the test

Lecture Notes 13

Lecture Notes 14

Lecture Notes 15

Lecture Notes 16

Lecture Notes 17

Lecture Notes 18

Lecture Notes 19

Lecture Notes 20

Lecture Notes 21

Lecture Notes 22

Lecture Notes 24

Lecture Notes 27

Nonparametric Regression

Causal Inference