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

• Autore: Andersson Jimmy
• Pubblicazione: Berkeley, CA : , : Apress L. P., , 2021; ©2022
• Descrizione fisica: 1 online resource (222 pages)
• Disciplina: 005.133
• Soggetto genere / forma: Electronic books.
• Nota di contenuto: Intro -- 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.
• Titolo autorizzato: Statistical Analysis with Swift
• ISBN: 1-4842-7765-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910508433603321
• Lo trovi qui: Univ. Federico II