This course teaches the fundamentals of statistics, that is, the ability to describe data samples and draw inferences about the populations from which they were drawn. It should also sharpen individual intuition about how to read data, interpret data, and judge others' claims about data.

• characterize population data intuitively for themselves and others;
• draw conclusions and inferences from population data;
• check assumptions of others' claims and debug their putative "facts";
• look for correlations while controlling for confounding effects

Prerequisites: none

Reading: We will be using two textbooks:

• Introductory Statistics for the Behavioral Sciences (5th Edition) by Welkowitz, Ewen, and Cohen (the publisher is just coming out with the 6th edition, but we'll be sticking with the 5th).
• Introductory Statistics with R (Paperback) by Peter Dalgaard

Both books will be available at Ulrich's.

Assignments and grading

Instructor: Lada Adamic, office hour Tuesday 10:30-11:30 am

Course Syllabus (click on PDF icon to download lab notes)

date subject reading assignment due 1 Tue 9/5 intro S.ch1   2 Thu 9/7 lab: descriptive statistics S.ch2-5,7, R.ch1, R.ch3   3 Tue 9/12 probability intro McClave & Sincich Ch 3   4 Thu 9/14 lab: discrete distributions R.ch2   5 Tue 9/19 continuous distributions S.ch9, A1* PS 1 (extension) 6 Thu 9/21 lab: scatter plots and transformed scores S.ch6 PS 2 7 Tue 9/26 sampling A2,A3*   8 Thu 9/28 examples of applications R.ch4 PS 3 9 Tue 10/3 concepts of statistical inference S.ch8   10 Thu 10/5 lab: outliers, confidence intervals R.ch5.103 PS 4 11 Tue 10/10 significance testing S.ch10, S.ch11   12 Thu 10/12 lab: one and two sample tests R.ch4 PS 5   Tue 10/17 --study break--     13 Thu 10/19 in-class midterm 14 Tue 10/24 simple linear regression S.ch12, S.ch13   15 Thu 10/26 lab: simple linear regression and correlation R.ch5.4 PS 6 project proposal 16 Tue 10/31 analysis of variance S.ch15 & ch 16   17 Thu 11/2 lab: analysis of variance R.ch6 PS 7 18 Tue 11/7 tabular data S.ch17   19 Thu 11/9 guest lecture: Mick McQuaid   article review 20 Tue 11/14 statistical communication A4*,A5*   21 Thu 11/16 lab: tabular data R.ch7 PS 8 22 Tue 11/21 power S.ch14 (R.ch 8)     Thu 11/23 -- Thanksgiving break--     23 Tue 11/28 logistic regression   PS 9 24 Thu 11/30 lab: multiple & logistic regression R.ch9, R.ch11   25 Tue 12/5 other random statistical tests   project report 26 Thu 12/7 student project presentations     27 Tue 12/12 review take home final given out due 12/19 *The following can be obtained from cTools: A1: Freakonomics Introduction: the hidden side of everything A2: Freakonomics 1. What do schoolteachers and sumo wrestlers have in common? A3: Feakonomics 5. What makes a perfect parent? A4: Fairness and the Assumptions of Economics Daniel Kahneman; Jack L. Knetsch; Richard H. Thaler The Journal of Business, Vol. 59, No. 4, Part 2, 1986 A5: Joel Best. 2004. “Chapter 1: Missing Numbers.” in More Damned Lies and Statistics. Berkeley and Los Angeles: University of California Press. The Tuesday lectures used slides from a previous version of this course taught by Paul Resnick. You can download the slides there.