Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms

Chapter 1: Swift Primer

- Introduction to Swift and its pros when working with large data sets

- Provided data sets and how to load them using the Decodable protocol

- Higher-Order Functions (map, filter, reduce, apply)

Chapter 2: Introduction to Probability and Random Variables

- What is a random variable?

- Sample spaces

- Laws and axioms of probability

- Variable Independence

- Conditional probability

Chapter 3: Distributions and Random Numbers

- Mass and density functions

- Discrete distributions

- Discrete uniform distribution

- Bernoulli trials

- Binomial distribution

- Poisson distribution

- Continuous distributions

- Continuous uniform distribution

- Exponential distribution

- Normal distribution

- Implement a random number generator that samples from a given distribution

Chapter 4: Predicting House Sale Prices with Linear Regression

- Central tendency measures

- Variance measures

- Association measures

- Stratification of data

- Linear regression

Chapter 5: Hypothesis Testing

- T Testing

- Null and Alternative Hypotheses

- P-value

- Determining sample sizes

Chapter 6: Data Compression Using Statistical Methods

- Measurement scales

- Calculate the distribution of example data

- Compute a Huffman Tree

- Encode the original data in a smaller package

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