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.

• construct a data sample appropriate for a given question/hypothesis and understand biases that can be introduced through sampling
• select appropriate methods to analyze such samples to determine whether the hypothesized effects are statistically significant
• critically analyze the sampling methods and analysis of others (e.g. don't take what the popular press tries to feed you about the latest health-related finding -- be able to read the source study yourself)
• stop worrying and love the data

Prerequisites: none

Instructor: Lada Adamic

Reading: There are two required textbooks (to be found at local bookstores):

• Se5 (5th edition) or Se6 (6th edition) Introductory Statistics for the Behavioral Sciences by Welkowitz, Ewen, and Cohen.
• Re1 (1st edition), Re2 (2nd edition) Introductory Statistics with R by Dalgaard

Accommodations for students with disabilities

Academic integrity policy

We will be using R in class. R is a statistical programming language, and it is open source. You should bring a laptop to every class for hands-on in-class exercises. If you don't have one, please contact the instructor to arrange for a loaner laptop during classtime.

Assignments and grading (students will complete a small group project)

NEW - see finished projects from Winter '09

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

date subject reading assignment due 1 Thu 1/7 intro S.ch1: Introduction   2 Tue 1/12 descriptive statistics S.ch2-5 (descriptive statistics) Re1.ch1: Basics or Re2.ch1: Basics and Re2.ch2: the R environment   3 Thu 1/14 probability intro McClave & Sincich Ch 3 (available on cTools) PS 1 due 1/18 4 Tue 1/19 discrete distributions: the binomial and hypergeometric Re1.ch2/Re2.ch3: probability and distributions McClave & Sincich Ch 4.1-4.5 (available on cTools)   5 Thu 1/21 practice with discrete distributions Se5.ch9/Se6.8: Normal distribution Se5.ch6/Se6:7: Z and T scores PS 2 due 1/25 6 Tue 1/26 poisson distribution, transformed scores and the normal distribution McClave & Sincich Ch 4.6: the poisson 7 Thu 1/28 graphical descriptions of data Se5.ch9/Se6.8: Additional techniques for describing batches of data Re1.ch3/Re2.ch4: descriptive statistics and graphics PS 3 due 2/1 8 Tue 2/2 sampling A1,A2,A3*   9 Thu 2/4 concepts of statistical inference Se5.ch8&ch9/Se6.ch9 PS 4 due 2/8 10 Tue 2/9 outliers, confidence intervals,significance testing get started early on Thursday's reading   11 Thu 2/11 one sample tests Se5/e6.ch10 Re1.ch4,Re2.ch5 PS 5 due 2/15 12 Tue 2/16 two sample tests Se5/e6.ch11   13 Thu 2/18 simple linear regression Se5/6.ch12&13 PS 6 due 2/22 form group & select topic 14 Tue 2/23 review for midterm catch-up on reading   15 Thu 2/25 midterm       Tue 3/2 -- winter break --       Thu 3/4 -- winter break --   16 Tue 3/9 more regression and correlation Re1.ch5,Re2.ch6 17 Thu 3/11 analysis of variance Se6.ch15 & ch 17 Se5.ch15 & ch 16 PS 7 due 3/15 18 Tue 3/16 more analysis of variance Re1.ch6, Re2.ch7 project progress report due 3/17 19 Thu 3/18 statistical communication (I) A4*,A5* article review due 3/22 20 Tue 3/23 discussion of article reviews     21 Thu 3/25 tabular data, chi-squared Se5.ch17, Se6.ch20 PS 8 due 3/29 22 Tue 3/30 more tabular data Re1.ch7, Re2.ch8   23 Thu 4/1 power, multiple regression Se5&Se6: ch14 PS 9 due 4/5 24 Tue 4/6 logistic regression Re1:ch9&ch11, Re2: ch10&ch12   25 Thu 4/8 more multiple & logistic regression   PS 10 due 4/12 26 Tue 4/13 student project presentations   27 Thu 4/15 student project presentations   project report due 4/19 28 Tue 4/20 review (leftovers in R:) take home final given out due 4/23 *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. Here are some practice exams: 2006: midterm (solution), final (solution) (tennisdata.txt, tennisballweights.txt, you need to email me for Pew Survey) 2008: midterm (solution), final (solution) (MovieGenresInAsia.txt, MoviesCountryGenre.txt, BoxBudgetRating.txt)