• Sep 9: Descriptive statistics: central tendency and spread.
  
• Readings: SE chapters 2
  
• For computing the mode of a sample, see the R code snippets page
  
• Data: Fisher telephone conversation corpus: table 1, table 2
  
• Do women talk more than men do? Here are some relevant Language Log posts:
• http://itre.cis.upenn.edu/~myl/languagelog/archives/003420.html
• http://itre.cis.upenn.edu/~myl/languagelog/archives/003607.html

• Sep 11: Descriptive statistics continued
• R worksheet: plotting
• R worksheet: descriptive statistics
  

Week 4

• Sep 15: Invited talk: Statistics in practice

• Sep 17: Invited talk: Statistics in practice
• Homework 1 due

Week 5

• Sep 22: Obtaining your data: Getting text into R
• R code: reading and processing text
  
  
• Sep 24: Obtaining your data, continued

Week 6

• Sep 29: Probability distributions.
  
• Readings: SE ch. 3, ALD ch. 3 pp. 44-63
• We talk in class about your project ideas
  

• Oct 1: Probability distributions continued. Samples and populations.
  

Week 7

• Oct 6: Statistical tests: the basic principle; significance thresholds. Also: starting on the t-test
  
• R code: the t-test
• Readings: SE ch. 4,5, 6
  
• https://xkcd.com/539/
• Homework 2 due
  
  
• Oct 8: The t-test, continued. Also confidence intervals
  
• Readings: SE ch. 7, 8, 9
• Data: Fisher word counts, Fisher telephone corpus, analyzed using LIWC (warning: big dataset!),  meta-data for the Fisher telephone corpus

• Initial project descriptions due
  

Week 8

• Oct 13: Comparing more than two data sets: ANOVA
• Pitfalls of statistical analysis: The problem of multiple testing https://xkcd.com/882/
• R code: ANOVA
  
• Readings: SE ch. 11, 12
  
  
• Oct 15: Correlation and linear regression
  
• Readings: SE ch. 20, 261-70
• R code: Correlation and linear regression
  
• https://xkcd.com/552/
  
• https://9gag.com/gag/2344861
• Language Log on correlation and causation
  
• And another note on correlation and causation: "Nobel prize winners eat more chocolate"??
  

Week 9

• Oct 20: more linear regression
• R code: linear regression
• R code: more linear regression
• Readings: ALD ch. 4 pp. 84 - 10; SE rest of ch. 20, ch. 21
  
• How not to do a scientific study: "p-value hacking"
• A  linear regression tutorial
  
  
• Oct 22: Logistic regression
  

Week 10

• Oct 27: Logistic regression in R
• Readings: ALD ch 6 pp 195-199, 202-203
• R code: logistic regression
  
  
• Oct 29: Model comparison
• Readings: ALD ch. 6 pp 174-188
• R code: model comparison
• Homework 3 due

Week 11

• Nov 3: Practising linear and logistic regression
  
  
• Nov 5: Statistical testing: chi-squared
  
• Readings: SE ch. 19
• Intermediate project reports due
  

Week 12

• :Nov 10: Classification
• R code: classification examples
  
• R code: classification and cross-validation
  
  
• Nov 12: Classification continued
  
• Data: Wine reviews and prices
• R code: classification with Naive Bayes

Week 13

• Nov 17: Clustering for data exploration
• R code: clustering
  
• Readings: ALD ch. 5 pp 138-148
  
  
• Nov 19: Topic modeling
• Topic modeling: a demo
  
• Homework 4 due
  

Week 14

• Nov 24: Statistical pitfalls to avoid
  
  
• Nov 26: Thanksgiving
  

Week 15

• Dec 1: Project presentations
• 14:00 Weisz
  
• 14:09 Cavasso/Ebmeier
  
• 14:18 Reinhard/Idonor
  
• 14:27 Donohoe
  
• 14:36 Bortner
  
• 14:45 Chew/Nelson
  
• 14:54 Dang/Zuniga
  
• 15:03 Barajas/Kuhn
  
• Dec 3: Project presentations
• 14:00 Moran
  
• 14:09 Radpour
  
• 14:18 Charles
  
• 14:27 Fofliger
  
• 14:36 Broomfield/Gavino
  
• 14:45 Adams/Walker
  
• 14:54 Paulter
  

Final report due: Monday December 14, 2015, end of day