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STATISTICS 512 - FALL 2003

APPLIED REGRESSION ANALYSIS

 STAT 512 syllabus   general class syllabus   scores   schedule  

 

  • PREPARATION

    • Make sure you are registered into this course.  If you are enrolled at Purdue University North Central (PNC), you can use SOLAR to do this.

    • Create (validate) your WebCT account. Create (validate), then log on to WebCT for this course between Monday, August 25 and Friday, August 29.  If you cannot create a WebCT account, look at the instructions first and then, if this fails, contact me immediately.  If you fail to create a WebCT account by 4 am (yes, 4am!) (CST), Friday, August 29th, I will not permit you to take this internet course.

    • Buy A TI-83 Calculator.  Buy either the TI-83 (or TI-83 plus, or TI-83 silver edition) calculator.  

    • Buy the main text from the PNC bookstore or amazon.com: Applied Linear Statistical Models (4th Edition).  The solution manual is also useful.  There are also two technical manuals used on the statistical programming language, SAS.

    • Check out both the STAT 512 syllabus and the general class syllabus

    • Check out frequently asked questions asked by students.

    • Check out previous course material for previous quizzes, scores, student evaluations and other course material.

    • If you are anxious to start before the semester begins, download "attendance1.pdf", "attendance2.pdf", ..., "attendance14.pdf" given below and start answering the questions.  The homework questions, "hmk1.pdf", ..., "hmk7.pdf" are also given below for you start working on, if you wish.  You cannot send me--I will not accept--your answers, though, unless you submit them through WebCT.  All answers must be submitted through WebCT; WebCT allows me to not only keep track of the material you submit but also allows me to grade your submissions.

  • WEEK 1. August 25,27

    Appendix A. Some Basic Results In Probability and Statistics (pages 1313-1332)

    • attendance1.pdf is a document which gives questions based on the lecture given this week.  It is for your information; it is not handed in. 

    • Answer the questions from hmk1.pdf  selected at random for you by the WebCT on or before 4 am (CST) Friday, September 5.  Use the WebCT quiz/homework link to submit your homework assignment; do not use the WebCT email to send me your homework assignment!  Submit as many times as you want before the deadline, and receive the highest score of all the submissions.

    • SAS-lab1.pdf is a document which gives a list of all of the SAS programs used in the attendance notes.  It is for your information; it is not handed in.

    • TI83-lab1.pdf is a document which gives a list of all the TI-83 instructions used this week.  It is for your information; it is not handed in.

    • Check how you did on your homework assignment by looking your score up at scores after 9 pm (CST) Friday September 12.  Use your ID number to locate your score under the column H1.

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  • Labor Day Holiday, September 1

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  • WEEK 2. September 3

    PART I. Simple Linear Regression

    Chapter 1. Linear Regression With One Predictor Variable

    Chapter 2. Inferences In Regression Analysis

    • attendance2.pdf not handed in

    • The WebCT quiz to be done on or before 4 am Friday, September 12.  One 20 minute timed submission is allowed; each student does the quiz by themselves with no help from others.

    • Check out practice quiz1.pdf  questions to help you prepare for this quiz; these questions are not handed in.

    • SAS-lab2.pdf not handed in.

    • TI83-lab2.pdf not handed in.

    • Check how you did on your quiz by looking your score up at scores after 9 pm (CST) Friday, September 12.  Use your ID number to locate your total score, which is given in the third-to-last column under TT, and your current grade, which is given under the next-to-last column under G.

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  • WEEK 4. September 15,17

    PART I. Simple Linear Regression

    Chapter 4. Simultaneous Inferences and Other Topics In Regression Analysis

    Chapter 5. Matrix Approach to Simple Linear Regression Analysis 

  • WEEK 5. September 22,24

    PART II. Multiple Linear Regression

    Chapter 6. Multiple Regression - I

  • WEEK 6. September 29; October 1

    PART II. Multiple Linear Regression

    Chapter 7. Multiple Regression - II

  • WEEK 7. October 6,8

    Multiple Linear Regression

    Chapter 8. Building the Regression Model I: Selection of Predictor Variables

  • Mid-semester Break, October 13,14

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  • WEEK 8. October 15

    PART II. Multiple Linear Regression

    Chapter 9. Building the Regression Model II: Diagnostics 

  • WEEK 9. October 20,22

    PART II. Multiple Linear Regression

    Chapter 10. Building the Regression Model III: Remedial Measures and Validation

  • WEEK 10. October 27,29

    PART II. Multiple Linear Regression

    Chapter 11. Qualitative Predictor Variables

  • WEEK 11. November 3,5

    PART II. Multiple Linear Regression

    Chapter 12. Autocorrelation in Time Series Data

  • WEEK 12. November 10,12

    PART III. Nonlinear Regression

    Chapter 13. Introduction to Nonlinear Regression

  • Last day to drop the course, November 21

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  • WEEK 13. November 17,19

    PART III. Nonlinear Regression

    Chapter 14. Logistic Regression, Poisson Regression, and Generalized Linear Models

  • Thanksgiving Vacation, November 26-28

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  • WEEK 14. November 24

    PART IV. Correlation Analysis

    Analysis of Variance: I

    Chapter 15. Normal Correlation Models

  • WEEK 15. December 1,3

    Review

  • WEEK 16. December 8,10

    Review

    • Do you know when and where you are going to write your supervised final exam? 

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  • WEEK 17. December 13-19

    Final Exam