- There are two textbooks for this course
- OpenIntro: You can download this textbook for FREE. In addition to the textbook, their website has additional lecture slides, video lectures, and labs for practicing R. This book will generally be used for everything but probability.
- From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science: Again, this book can be downloaded entirely for free. This book will be useful for the more mathematical introduction to probability that is beyond the scope of OpenIntro.

- A fast, practical way to learn R is through the swirl package. Follow the instructions from that link to go through a few tutorials.
- Rstudio has made some very useful cheat sheets that you can reference while you are programming in R. I’ve placed some of the more useful ones for this class here:
- In addition, I wrote up a brief cheat sheet for R Base graphics.

Note: Each lecture is a topic, not a day of class.

- Course Outline
- Data Basics
- Histograms
- Center/Spread
- Quantiles
- Categorical Variables
- Case Study: Trump’s Tweets
- Densities
- Normal Density Calculations
- Sampling Distributions
- Probability and Sets
- Proofs from Axioms of Probability
- Conditional Probability and Independence
- Bayes Rule
- Discrete Random Variables
- Binomial Distribution
- Continuous Random Variables
- Multiple Random Variables
- Moment Generating Functions
- Central Limit Theorem Motivation
- Central Limit Theorem Proof
- Linear Approximation to Exponential
- Exponential Approximation
- Interval Estimates for Means
- Some Confidence Interval Examples
- Hypothesis Testing for Means
- Inference for Means in Small Samples
- Differences of Means
- Inference for Proportions
- Simple Linear Regression
- Multiple Linear Regression
- Review

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