Fall 2012, T 6:30–9:20, room 283A (Stan Brown)
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Throughout the Course
Used throughout the course
- Recommended Statistics Books
- UCLA Statistics Case Studies — real-life applications you can work yourself (accessed 2012-03-25)
- You can’t trust Excel to do critical statistics. See these
Heiser (accessed 2012-03-25) and
P.J. Wells (accessed 2012-03-25). (Excel 2010 has some new statistics functions that may address the problem. I haven’t seen any definitive papers yet.)
- On-line statistics courses:
Chapter 1: Data Collection
Chapter 2: Graphical Summaries
- The Joy of Stats video: Hans Rosling shows lots of great ways to present data (accessed 2012-03-25). Don’t miss the segment “200 Countries, 200 Years, 4 Minutes”, also available separately (accessed 2012-03-25).
- just for fun: self-referential graphs
- The Best Stats You’ve Ever Seen video: Hans Rosling uses descriptive stats to explode the myth of First World and Third World (accessed 2012-03-25).
- MATH200A Program part 1 can be used to make histograms, and
MATH200B Program part 2 graphs time series.
- ticalc.org has five programs to make pie charts. Though I haven't evaluated them in depth, PIEGRAPH.ZIP looks most interesting based on the screen shots.
- STEMLEAF.ZIP (1 KB) is a TI-83/84 program that makes stemplots
- Charles Minard’s famous graph of
Napoleon’s Moscow campaign (accessed 2012-03-25)
This is one of the many examples in Edward Tufte’s
The Visual Display of Quantitative Information.
Chapter 3: Numeric Summaries
Chapter 4: Correlation & Regression
Chapter 5: Probability
Chapter 6: Discrete Probability Distributions
Chapter 7: Normal Distribution
Chapter 8: Sampling Distributions and Sample Variability
- The Behavior of the Sample Mean (accessed 2012-03-25) — pictures show how sample means are distributed and why that distribution is different from the shape of the population.
Chapter 9: Confidence Intervals
Chapters 9–12: Inference
- These two handouts apply to Chapters 9 through 12, and they help you choose the right case for analyzing a given situation:
- Hypothesis testing a/k/a significance testing has a language and philosophy of its own, but once you get it you’ll find it’s the same for Chapters 10 through 12.
The following handouts
replace sections 10.1 and 10.2 of your textbook, and they're
included in Hypothesis Tests listed under Chapter 10 below:
- Look at this one after you’ve spent a week or two on hypothesis tests: Top 10 Mistakes of Hypothesis Tests
Chapter 10: Hypothesis Tests for One Pop.
- The logic of hypothesis testing can seem very strange at first. One way to demystify it is to read many different writeups, not just your textbook. Here are two easy and excellent ones:
Is Statistics Hard? by Gerald Dallal (accessed 2012-03-25)
Logic of Hypothesis Testing, part of HyperStat (accessed 2012-03-25).
- When α is the standard 0.05, one in 20 of your significant results are actually Type I errors. How do you interpret published results, then? See Sifting the evidence—what‘s wrong with significance tests? by Sterne and Smith (accessed 2012-03-25).
- Even professional researchers can make mistakes and publish false results through misusing hypothesis tests. “Data mining” (first gathering data, then looking for relationships) is one problem but not the only one. See Why Most Published Research Findings Are False by John Ioannidis (accessed 2012-03-25). If you find the article heavy going, just scroll down to read the example in Box 1 and then the corollaries that follow.
- You can also do hypothesis tests about σ, the
population s.d.: see MATH200B Program part 5 or Inferences about One Population Standard Deviation.
Chapter 11: Inferences on Two Samples
Chapter 12: Additional Inferences
These are not part of the course syllablus, but are included for those who would like to learn more.
Review for the Exam
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