Stat 704 - Fall 2015

Welcome to Stat 704, Data Analysis I. This page contains updates to the course syllabus, computer notes from class and homework assignments. Send any questions to grego@stat.sc.edu

Page revised on 6/24/2015

• Syllabus

• Notices

• Homework
  • Homework 1

  • Homework 2

  • Homework 3

  • Homework 4

  • Homework 5

• Class Exercises
  • Class Exercise 1

  • Class Exercise 2

  • Class Exercise 3

  • Class Exercise 4

  • Class Exercise 5

  • Class Exercise 6

  • Class Exercise 7

  • Class Exercise 8

  • Class Exercise 9 (In-class computer exercise)

  • Class Exercise 10

  • Class Exercise 11 (In-class computer exercise)

• Lectures
  • Lecture 1 Random variables and expectation

  • Lecture 2 One and two-sample testing and diagnostics

  • Lecture 3 Nonparametric tests

  • Chapter 1 notes Linear regression with one predictor variable

  • Chapter 2 notes, Part I Inferences in regression and correlation analysis

  • Chapter 2 notes, Part II Inferences in regression and correlation analysis

  • Chapter 5 notes Matrix approach to simple linear regression analysis

  • Chapter 5 notes Multivariate Normal Distribution

  • Chapter 6 notes Multiple Regression I

  • Chapter 6 notes Multiple Regression I, continued

  • Chapter 6 and Chapter 3 notes Multiple Regression I: Transformations

  • Chapter 7 notes Multiple Regression II

  • Chapter 8 notes Regression Models for Quantitative and Qualitative Predictors

  • Chapter 9 notes Building the Regression Model I: Model Selection

  • Chapter 10 notes Building the Regression Model II: Diagnostics

  • Section 11.1-11.2 notes Building the Regression Model III: Remedial Measures

  • Section 11.3 notes Building the Regression Model III: Remedial Measures

  • Generalized Additive Models notes

  • Section 11.4-11.5 notes Building the Regression Model III: Local Smoothing and the Bootstrap

• Computer Code, Data Sets and Notes
  • Tab-delimited text file of sample of Fall 2008 cohort

  • Tab-delimited text file of same data set as above with additional variables

  • Tab-delimited text file 2009 follow-up to sample of Fall 2008 cohort

  • Tab-delimited text file 2010 follow-up to sample of Fall 2008 cohort

  • Excel workbook for bacterial water quality data

  • Comma-delimited text file for SCDOT 2012 traffic count data

  • CE 1 worksheet: Inputting data sets in SAS and R

  • CE 2 worksheet: Merging data sets in SAS

  • SAS code for t confidence intervals

  • R code for t confidence intervals

  • SAS code for paired t-tests

  • R code for paired t-tests

  • SAS code for two sample t-tests

  • R code for two sample t-tests

  • SAS code for one sample and two sample nonparametric tests

  • R code for one sample and two sample nonparametric tests

  • SAS code for whale selenium plots in Chapter 1 lecture

  • R code for whale selenium plots in Chapter 1 lecture

  • SAS code for Toluca data in Chapter 2 lecture

  • R code for Toluca data in Chapter 2 lecture

  • SAS code for Celt and Coleman data in Chapter 5 lecture

  • R code for Celt and Coleman data in Chapter 5 lecture

  • R code for standard matrix functions in Chapter 5

  • R code for Chapter 5 multivariate normal lecture graphics

  • SAS code for Chapter 6 multivariate regression lecture

  • R code for Chapter 6 multiple regression lecture

  • R code for Chapter 6 multiple regression surface plot lecture graphic

  • SAS code for Chapter 3 and Chapter 6 multiple regression transformations lecture

  • SAS code for Chapter 7 multiple regression lecture

  • SAS code for Chapter 8 qualitative/quantitative predictors lecture

  • SAS code for Chapter 9 model selection lecture

  • SAS code for Chapter 10 diagnostics lecture

  • Other diagnostics examples from Prof. David Hitchcock's STAT 704 webpage.

  • Tab-delimited text file of Salary data set used in Chapter 10 SAS code.

  • SAS code for Chapter 11 WLS and Ridge Regression lecture

  • R code and data set for Chapter 11 ridge regression

  • SAS code for Chapter 11 robust regression lecture

  • SAS code for Generalized Additive Models lecture

  • SAS code and data set for Local Robust Regression lecture

  • R code for bootstrapping and local smoothing

• Exams
  • 2015 Test with Problem 1 data set and Problem 2 data set

  • 2015 Exam with data set

• Project
  • Proposal and Project guide

  • Oral Report Guidelines

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