8/29/2022

Discussion of R code to simulate Pi - I will ask for volunteers to discuss their code on Wed.

Comments on your Reading Assignment

• Data science versus statistics
• Case Study - sabermetrics (Bill James)
  • Money Ball (Billy Bean)
  • Buffalo Bills (Dennis Lock)
  • Charlotte Hornets (Alexander Powell)
• tidyverse and piping
• Questions?

Loading packages

• mosaic and mosaicData - lots of useful tools and resources
• mdsr - resources for our textbook
• fed16 (fed, fed12, etc.) - Federal Elections Commission
• macleish - field station weather data
• IMDbPY - Python package for internet movie database
• LOTS of others (later)

Question for the class

• For those of you with R and RStudio experience, what is one tip for new users of RStudio? That is, what is one thing that you wish you would have been told when you started using R and RStudio?

Class Activity - Our first set of simulations

• Generate 100 uniform(0, 1) random numbers using runif().
  • Summarize the distribution with a histogram. Does the shape of the distribution match what you expected?
  • Comment on the center, shape, and spread.
  • Use qqnorm() and qqline() to check normality.
  • Use t.test to check if the random number generator matches the expected mean.
  • Create a new variable y = 2x that doubles the range of simulated values
  • Create a new variable that generates uniform random numbers between -3 and 3.
• Generate 100 normal(0, 1) random numbers using rnorm().
  • Summarize the distribution with a histogram. Does the shape of the distribution match what you expected?
  • Comment on the center, shape, and spread.
  • Use qqnorm() and qqline() to check normality.
  • Change the mean and standard deviation to other values of your choice. Does the shape of the quantile plot change as you change the mean and standard deviation?
• Generate 100 binomial(31, .5) random numbers using rbinom().
  • Summarize the distribution with a histogram.
  • Comment on the center, shape, and spread.
  • Use qqnorm() and qqline() to check normality. Is the distribution approximately normal?
  • Change the number of trials and the probability of success to other values of your choice. Does the shape of the distribution change as you change the values of n and p? Make sure that you try some values of p close to zero and others close to 1.
• Generate 100 geometric(1/6) random numbers using rgeom().
  • Summarize the distribution with a histogram.
  • Comment on the center, shape, and spread
  • Use qqnorm() and qqline() to check normality. Is the distribution approximately normal?

Please complete your reading of Appendix B and read Chapter 2 for class on Wednesday.