Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

This is general advice going forward but it makes sense to include it here: My advice is to start working through the programming challenges first. The programming challenges will only include material that we covered in the readings for the previous week.

If you're having trouble loading up your dataset (PC2) please find me or Jeremy for help asap because the other questions depend on doing this successfully.

Programming Challenges

 * PC0. Create a new project and RMarkdown script for this week's problem set.
 * PC1. Navigate to and download the RData file in the   subdirectory with your name associated with it.
 * PC2. Load that file into R. It should load up one variable. Find that variable!
 * PC3. Compute and present summary statistics for your variable. Be sure to include the minimum, maximum, mean, median, variance, standard deviation, and interquartile range.
 * PC4. Write your own functions in R to re-compute the mean and the median. Be ready to walk us through how your function works.
 * PC5. Create some visualizations of your dataset: at the very least, create a boxplot and histogram.
 * PC6. Some of you will have negative numbers. Whoops! Recode all negative numbers as missing (i.e. NA) in your dataset. Now compute a new mean and standard deviation. How does it change? (Hint: the  function may now produce an error. You have to include the argument   to work around this.)
 * PC7. Log transform your dataset. Create new histograms and boxplots, as well as new mean, median, and standard deviation.
 * PC8. Briefly discuss any important differences between the original data and the log-transformed data.
 * PC9. Save your work and archive the project (i.e., in a .zip file) and upload it to canvas.

Exercises from OpenIntro §2

 * Q0. Any questions or clarifications from the OpenIntro text or lecture notes?
 * Q1. Exercise 2.12 on kids missing school
 * Q2. Exercise 2.20 on "assortative mating"
 * Q3. Exercise 2.26 on twins (and conditional probability)
 * Q4. Exercise 2.32 on the birthday problem (This is a super famous problem! Don't look it up!)
 * Q5. Exercise 2.38 with the example of baggage fees
 * Q6. Exercise 2.44 on income and gender

Empirical Paper
Let's take a look at this paper which is the second paper I published in graduate school (!):


 * Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in Proceedings of the 8th ACM Conference on Designing Interactive Systems. Aarhus, Denmark: ACM. [PDF available on my personal website]

At the very least read enough of the paper to get a sense for what it's about and to understand Table 2 but we the questions here are all basically going to be drawn from that table. It might be expedient to read the blog post that I wrote about this. Feel free to ignore all the stats or other stuff that's not relevant.

Looking at data from the US, answer a few questions we didn't answer in the question but that seem like they might be interesting (I has basically only finished taking a class like this one at the time I wrote this this paper!)


 * Q7. Given that a US customer in the dataset has bought a LilyPad (either alone or in combination with a "normal" Arduino), what is the probability of that that they are female?
 * Q8. Given that a US customer in the dataset is female, what is the probability that they bought a LilyPad (either alone or in combination with a "normal" Arduino)?
 * Q9. In substantive terms, do these two numbers evidence of that the LilyPad design is successfully appealing to women? Ideally, you should be prepared to present at least one reason why and one one reason you might be skeptical.