assignments

homework 1

• Calculate the mean, variance and standard deviation of the ratings for Cookie C (data available on the "our code & data" page). Do all of these twice: once "by hand" (i.e. like we did in class. Calculators are allowed but work out all of the intermediate numbers) and once in R.
• From OpenIntroStats v3 Section 1.9 do Exercises 1.2, 1.7 (data is built into R with the variable name "iris"), 1.19, 1.25, and 1.54.
• Using the built-in ChickWeight data in R (see https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/ChickWeight.html for details), make a plot that reveals something about the data, and explain what your plot shows.

quiz 1

• a place to record the grades

homework 2

• Do an analysis of any one of the pig-like games presented Thursday, using probability and/or trial and error to come up with an effective strategy, and explain.
• Do something neat with R that you couldn't do previously. You might work on making better graphs, or find some code online to do something fun and get it to work (and maybe adjust it to do something else), follow a tutorial through in manipulating data, or whatever else.
• Do exercises 3.7 and 3.10 from the textbook.

project proposal

• Describe what you have in mind for your term project. At this point you can be fairly vague. The final result should
  • be an analysis of some data in which you argue for some specific conclusion,
  • roughly the size of a 5 page paper
  • contain data, perhaps from an online source
  • have a bibliography of sources (both data & methods)
  • include your methods, graphs, and reasoning

homework 3

• Do exercises 4.4, 4.23, 4.25, and 4.30 from the end of the chapter (pages 204 and on) in the textbook.
• As discussed in class, use a hypothesis test on the difference of means with this alcohol.csv data to decide where there there is evidence that drinking affects memory. The columns are : (memory = score on a memory test, alcohol = whether or not that person consumed alcohol before the test). (Remember that you can import this into R with the read.csv function. If you use the online RStudio, you'll also need to upload the file first.)

quiz 2

• a place to record the grades.

stats project

• The written version should be turned in (email is fine) by Friday May 6.
• Oral summary presentations will happen the last day of class, Tue May 3.
• Quesions? Ask.

homework 4 (optional)

• (Doing this can improve your grade. Not doing it will not bring your grade down.)
• ANOVA : do end of chapter exercises 5.50 and 5.51 in our textbook.
• Chi Square : do end of chapter exercises 6.41 and 6.42 in our textbook.
• linear regression : Look at (x1,y1) and (x2,y2) in the built-in R "anscombe" dataset, which has four sets of (x,y) values. Type "anscombe" (no quotes) in R to see this built-in dataframe. (The full name of x1 for instance is anscombe$x1 .)
  • Use (at least) R's cor(x,y) and summary(lm(y ~ x)) and plot(x, y) to compare (x1,y1) and (x2,y2). Explain what's going on.
  • What would you expect the y value for x=16 would be for in those two cases?

final exam

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semester grade

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