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Summary of marks

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• Course Outline
• Final exam information page
• SAS OnDemand: Run SAS for free online.
• Brief instructions for using SAS OnDemand.
• Formula Sheet        PDF      LaTeX
• Assignments
  1. Assignment 1 (Introduction and vocabulary): Quiz on Friday January 19th in tutorial.
  2. Assignment 2 (Elementary tests): Quiz on Friday January 26th in tutorial.
  3. Assignment 3 (One-way ANOVA with multiple comparisons): Quiz on Friday February 2nd in tutorial. Note that the quiz will be in DH2060, just this once.
  4. Assignment 4 (Permutation and randomization tests): Quiz on Friday February 9th in tutorial.
  5. Assignment 5 (Regression concepts): Quiz on Friday February 16th in tutorial.
  6. Assignment 6 (Regression on the Diet data, more concepts): Quiz on Friday March 1st in tutorial.
  7. Assignment 7 (Regression on the Sales and Diversity data, including interactions with cell means coding, and cross-validation): Quiz on Friday March 8th in tutorial.
  8. Assignment 8 (Prediction for the Diversity data, Time series on the Raptors data).
  9. Assignment 9 (Logistic Regression).
  10. Assignment 10 (Factorial ANOVA).
  11. Assignment 11 (Multivariate approach to repeated measures, a few extras). There will be no quiz on this one; it's just practice for the final exam. Here are my
      • Answers to Questions 1 and 2
      • SAS for Question 3.
  
  
• Lectures
  1. Introduction to data analysis: See Chapter 1.
     • PDF      PowerPoint
  2. Introduction to SAS: See Chapter 2.
     • PDF      View LaTeX     
  3. SAS Example One: Basic descriptive statistics on the cars data. See Chapter 2.
  4. SAS Example Two: Explore the SENIC data with elementary tests. See Chapter 2.
  5. One-way ANOVA with multiple comparisons: Concepts. See Chapter 3.
     • PDF      PowerPoint
  6. SAS Example Three: One-way ANOVA with multiple comparisons. See Chapter 3.
  7. Permutation and Randomization Tests: Concepts. See Chapter 11, except that Chapter 11 uses R and not SAS.
     • PDF      LaTeX      Download zip file including graphics
  8. SAS Example Four: Permutation and randomization tests.
  9. Multiple Regression: Concepts. See Chapter 5.
     • PDF      PowerPoint
  10. SAS Example Five: Multiple regression on the cars data.
  11. SAS Example Six: Introduction to the math data.
  12. SAS Example Seven: Regression on the exploratory math data: Part One
  13. Basic Regression Diagnostics. This part of the text is not written yet.
      • PDF      LaTeX      Download zip file including graphics
  14. SAS Example Eight: Regression on the exploratory math data: Part Two (Diagnostics)
  15. SAS Example Nine: Regression on the math data: Part Three (Replication and prediction)
  16. Introduction to Time Series.
      • PDF      LaTeX      Download zip file including graphics
  17. SAS Example Ten: Time series analysis with proc autoreg
  18. Logistic Regression: Concepts. See Chapter 6.
      • PDF      PowerPoint
  19. SAS Example Eleven: Logistic Regression on the math data: Part One
  20. SAS Example Twelve: Logistic Regression on the math data: Part Two
  21. Factorial ANOVA: Concepts. See Chapter 7.
      • PDF      PowerPoint
  22. SAS Example Thirteen: Factorial ANOVA on the potato data.
  23. Multivariate Regression and ANOVA: Concepts. See Chapter 9.
      • PDF      PowerPoint
  24. SAS Example Fourteen: Multivariate analysis of the Little Tubes data.
  25. Within Cases, Part One: Concepts. See Chapter 9.
      • PDF      LaTeX      Download zip file including graphics
  26. SAS Example Fifteen: Within Cases Part One (Multivariate approach) with SAS.
  27. Within Cases, Part Two: Concepts. See Chapter 9.
      • PDF      PowerPoint
  28. SAS Example Sixteen: Within Cases Part Two (Covariance structure approach) with SAS.
  
  
• Textbook
  • Chapter 1: Vocabulary and basic concepts used throughout the course, and also a brief discussion of elementary significance tests.
  • Chapter 2: Introduction to SAS. The unix/linux details do not apply to this class.
  • Chapter 3: Comparing several means.
  • Chapter 11: Permutation and randomization tests (using R).
  • Chapter 4: Simple categorical data analysis of the Berkeley graduate admissions data
  • Chapter 5: Multiple regression
  • Chapter 6: Logistic regression
  • Chapter 7: Factorial analysis of variance
  • Chapter 9: Multivariate and within-cases
  • Home page for the textbook. This has the current version of the full book. You are responsible for the individual chapters posted above.

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