SI 544 Introduction to Statistics and Data Analysis


SI 544 home

Readings, assignments, etc. will be posted to the course ctools website

problem sets

software tools for the class

Lada Adamic

Tracy Liu


Winter 2010:

Lectures will be
Tuesdays and Thursdays
from 8:30 to 10:00 am.
Location WH 311

Discussion section Thursdays 6:30-7:30 WH409

Office hours:
Lada: Mon 11am-12pm & Fri 1:30-2:30pm in WH3082
Tracy: Tues 10-11am WH417A
Thurs 7:30-8:30 WH409




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.

Learning objectives. At the end of this course students should be able to:
  • 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
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)