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

Description

Table of Contents About the Author About the Technical Reviewer Acknowledgments Chapter 1: Swift Primer A Swift Overview Performance Safety Correctness Hardware Acceleration Swift Package Manager Conclusion Working with Swift Data Formats The Code Project The Decodable Protocol The KeyPath Type Higher-Order Functions Chapter Summary Chapter 2: Introduction to Probability and Random Variables Probability Sample Spaces Events The General Addition Rule Conditional Probabilities Independence Bayes’ Theorem Random Variables Discrete vs. Continuous Random Variables Chapter Summary Chapter 3: Distributions What Is a Distribution? Discrete Distributions Bernoulli Distribution and Trials Geometric Distribution Binomial Distribution Distributions Application Continuous Distributions Differences from Discrete Distributions Exponential Distribution Normal Distribution Expected Value Variance and Standard Deviation Chapter Summary Chapter 4: Predicting House Sale Prices with Linear Regression Linear Regression Splines Regression Techniques Loss Function Finding an Optimal Solution Implementing Simple Linear Regression Multiple Linear Regression Deriving Linear Regression with Vectors Implementing Multiple Linear Regression Predicting House Sale Prices Chapter Summary Chapter 5: Hypothesis Testing What Is Hypothesis Testing? Formulating Hypotheses The Null Hypothesis The Alternative Hypothesis Tails Distribution of Sample Means The Central Limit Theorem Testing the Hypothesis Determining Confidence Levels Determining Alpha Values Performing the Test Determining the P-value Standardization Computing a Standard Score Computing Confidence Intervals A Word on Chi-Squared Tests Chapter Summary Chapter 6: Statistical Methods for Data Compression An Introduction to Compression Function Behaviors Lossless vs. Lossy Compression Huffman Coding Storing the Huffman Tree Implementing a Compression Algorithm The Compression Stage The Decompression Stage Chapter Summary Chapter 7: Statistical Methods in Recommender Systems Recommender Systems The Functions of Recommender Systems Approaching the Problem First Approach Second Approach Final Approach Similarity Measures Cosine Similarity Euclidean Squared Distance Expected Ratings Laplace Smoothing Rating Probabilities Implementing the Algorithm The Main Program Chapter Summary Chapter 8: Reflections The Swift Programming Language Probability Theory Distributions Regression Techniques Hypothesis Testing Statistical Methods for Data Compression Statistical Methods in Recommender Systems Professional Areas of Application Data Scientist Machine Learning Engineer Data Engineer Data Analyst Topics for Further Studies Numerical Linear Algebra Multivariate Statistics Supervised Machine Learning Index