G492 /G751 Eric Rasmusen, erasmuse@indiana 29 September 2009 Statistics and Regression

1. G492 /G751 Eric Rasmusen, erasmuse@indiana.edu 29 September 2009 Statistics and Regression

2. Professor Rasmusen, Greetings, I hope all is well.  I was your student in G492 and G406 in the Spring of 2007 – so it has been a while.  I wrote my thesis for your class on Charter Schools, in case that rings any bells.  I was writing because a short series of events prompted me to want to contact the BEPP program at Kelley.  I graduated last May from Kelley with Honors through the Mitte Program, and distinction in my class.  I fully enjoyed the entire program and BEPP offered an outstanding opportunity to couple my business degree with economic and policy education.  I currently am a Congressional Staffer working in Washington for Congressman John W. Olver of Massachusetts.  Just this morning I was working on some data for the Congressman and used many of the tools I learned in Kelley, and subsequently impressed the audience in our meeting.  After coming out of the meeting I felt a great sense of pride that I could use what I learned to “show off” my Kelley education (most of my colleagues have gone to small east coast private schools).  I went online and visited the BEPP website out of curiosity and noticed there is a listing of recent and older graduates who have gone through BEPP.  Most of my colleagues went on to Consulting Firms and other Private Sector options and I think it could be beneficial to the program to also highlight a student who went on to work in policy. I wasn’t certain who to contact with this thought, so I considered writing to you, my former professor, and seeing if you could forward this on to whomever would be in charge of the website’s information.  I hope the semester has started off well, and Bloomington is warming up quickly! Thank you again, Jason Powers Office of Rep. John W. Olver 1111 Longworth HOB Washington, DC  20515 (202) 225-5335 (phone) (202) 226-1224 (fax)

3. How would you explain how regressions work? Here is a fun way.

4. What does it mean to use a test that rejects the hypothesis that a coin is fair, at the 5% significance level?

5. What does it mean to use a test that rejects the hypothesis that a coin is fair, at the 5% significance level? ---That the test will falsely reject 5% of the times it is used. NOT the probability that the null hypothesis is true, given that you accepted it.

6. Consider testing whether a coin is fair by flipping once and rejecting fairness if Heads comes up. That test will falsely reject 50% of the time. Even a test that says you reject fairness if Heads comes up more than 100 out of 110 throws will still reject falsely with some probability. Rice Applet: Confidence Levels

7. KNOW THY DATA! In being persuasive, data management is important. Anyone can understand a data entry mistake. That destroys credibility. Maybe have one member of the team just for data.

8. ETHICS “… the problem is not that one is always being asked to step across a well-defined line by unscrupulous lawyers. Rather, it is that one becomes caught up in the adversary proceeding itself and acquires the desire to win. …” “Continuing to regard oneself as objective, one can slip little by little from true objectivity.”

9. An Excel file with the data is here.

10. An Excel file with the data is here.

11. An Excel file with the data is here. The correlation coefficient is the square root of the R2 for a regression of one variable on a constant and the other variable.

12. An Excel file with the data is here. If income per capita rises by xxx, how much does the abortion rate rise?

13. Principles of Regression Presentation • Only present relevant and meaningful numbers. 2. Do not write 1.23423 when rounding to 1.23 will do just as well. Fewer digits yield greater clarity. 3. Use correlation matrices to show the simple correlations between important variables. 4. Give summary statistics. Think about which are most useful. Think about presenting the mean, median, mode, minimum, maximum, standard deviations, and number of observations. Do not present all of these--think.

14. 5. Use words for variable names, not computer codes. 6. Present the coefficients, standard errors or t-statistics (not both), R2, and number of observations. Do not present other statistics (e.g. an F-test for all coefficients equalling zero) unless you have a reason to. 7. If the left-hand variable (y-variable, dependent variable, endogenous variable) takes only a few values (e.g., 0 and 1) then use a special technique such as logit or tobit. If a right-hand variable (x-variable, independent variable, exogenous variable) takes only a few values, that does not create a need to use anything besides OLS.

15. Consider using nonlinear specifications, as illustrated in the java applet at http://www.ruf.rice.edu/~lane/stat_sim/transformations/index.html

16. Principles of Tables 1. Keep the data-to-ink ratio high. 2. Leave out dividing lines and boxes unless you have a good reason for them. 3. Leave off repetitive, useless numbers. 4. Don't use just capital letters. 5. Circle or otherwise mark important numbers, in particular, ones you mention in the text or talk. 6. Make the table self-contained. Don't require the reader to refer to the text or a previous table. Include the source and the units of measurement. 7. Number and title every table.

17. Uniform Crime Reports: Crime in the United States, Section IV

18. Regression Tables • Ginger Jin and Phillip Leslie (2003): "The Effect of Information on Product Quality: Evidence from Restaurant Hygiene Grade Cards,"Quarterly Journal of Economics, 118(2): 409-451(May 2003)