Stat 20: some review for midterm 1
Michael Lugo
September 27, 2010
The exam covers Chapters 1 through 5 and 8 through 11 of the text. Here
I’ll briefly summarize the things you should know from each chapter.
Chapter 1: Controlled Experiments
• what is a controlled experiment?
• some important words: placebo, control, treatment, confounder, doubleblind
• don’t need to know the details of specific experiments
Chapter 2: Observational Studies
• what is an observational study?
• what are some pitfalls of observational studies? (adherence, Simpson’s
paradox, doctors preferentially seeing patients who are in trouble, and
so on)
Chapter 3: Histograms
• how to draw a histogram. Make sure you can label the axes correctly!
• what happens to the histogram when we transform the data?
• how to determine the portion of a population which lies in some interval
[a, n] (area under a curve)
• different kinds of variables: discrete vs. continuous, qualitative vs.
quantitative
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Chapter 4: Averages and standard deviations
• how to compute them. (and the RMS)
• relationship between averages and histograms (“balancing” interpretation)
• how do the average and the SD change with a change of scale?
Chapter 5: The normal approximation
• standardizing data
• how to find areas under the (standard) normal curve
• finding areas under the normal curve for a given mean and SD
• percentiles and the normal curve
Chapter 8: correlation
• drawing and interpreting scatter diagrams
• the elliptical “cartoon” of a scatter diagram. where are the SD, regression lines on such a cartoon?
• interpretation of correlation coefficients: what does it mean if r is positive? negative? close to 1? close to 0?
• how to compute correlation coefficients (there are two ways; either one
is fine)
Chapter 9: more about correlation
• what happens to r when data undergoes a change of scale?
• when is it appropriate to use r as a measure of association? (only in
the linear case)
• ecological correlation
• association is not causation
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Chapter 10: regression
• how to make predictions using regression
• regression line is the smoothed graph of averages
• regression to the mean, and the regression fallacy
• why are there two regression lines?
Chapter 11: RMS error for regression
• what is the RMS error of a prediction from regression?
• using the normal curve in a vertical strip
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