1. STAT 104: Section 3 21 Feb, 2008 TF: Daniel Moon

2. TF Office hour: Th. PM 1:00-2:00, 6 th Floor, 601

3. Agenda of Today Review Feedback from Hw #2 Examples from 1 st Mid-term Exam Principles of experimental design Control Randomization Replication Improvements Blocking (Stratification) Placebo Effect

4. Linear Regression Assumptions Residuals have en expected value 0. Residuals are uncorrelated. Residuals have the same variance. To check these assumptions: Residual plot (residuals vs. X): constant variance Normal Prob. Plot: Normality

5. Linear Regression Assumptions

6. Feedback on HW #2 When writing regression equation, don't forget y_hat, if you don't include error term. (Right: "y = a + bx + error" or "y_hat = a + bx") R is unit-free  Problem 4.e. What is r^2? (Y_hat = aX + b) One unit increase in X leads to "a" unit increase in Y. (not clear answer: for every $1 of revenue, the value of t eam goes up $2.59) (--> (It's better say) for every $1 revenue increase, ")

7. Examples (2007 Midterm 1)

10. Review Feedback from Hw #2 Examples from 1 st Mid-term Exam Principles of experimental design Control Randomization Replication Improvements Blocking (Stratification) Placebo Effect

11. Sources of Data Anecdotal Information Available Data Observational Studies Controlled Experiments Randomized Controlled Experiments

12. Elements of Designing an Experiment Subjects to be tested Observational or Randomized Experimental? How many individuals? How will the individuals selected? What kind of variables measured?

13. Principles of experimental design

14. Control

15. Randomization

16. Randomization Example

17. Review Feedback from Hw #2 Examples from 1 st Mid-term Exam Principles of experimental design Control Randomization Replication Improvements Blocking (Stratification) Placebo Effect

18. Blocking

19. Blocking Example

20. Examples (2007 Midterm 1) Physical ActivityBreast Cancer

21. Examples (2007 Midterm 1) Observational Study Lurking Variables Other good lifestyle habits

22. Examples (2007 Midterm 1)

24. Factors that we should consider Sample Size Individuals Experimental Design Individuals Selected Response Variable

25. Examples (2006 Midterm 1) Breakfast Performance

26. Examples (2006 Midterm 1)