Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms
            
暫譯: 使用 Swift 進行統計分析:在 Apple 平台上的數據集、統計模型與預測
        
        Andersson, Jimmy
商品描述
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
- &nb商品描述(中文翻譯)
第 1 章:Swift 入門  
- 介紹 Swift 及其在處理大型數據集時的優勢  
- 提供數據集及如何使用 Decodable 協議加載它們  
- 高階函數 (map, filter, reduce, apply)
第 2 章:機率與隨機變數介紹  
- 什麼是隨機變數?  
- 樣本空間  
- 機率的法則與公理  
- 變數獨立性  
- 條件機率  
第 3 章:分佈與隨機數  
- 質量與密度函數  
- 離散分佈  
- 離散均勻分佈  
- Bernoulli 試驗  
- 二項分佈  
- Poisson 分佈  
- 連續分佈  
- 連續均勻分佈  
- 指數分佈  
- 常態分佈  
- 實作一個從給定分佈中取樣的隨機數生成器  
第 4 章:使用線性回歸預測房屋銷售價格  
- 中心趨勢測量  
- 變異數測量  
- 關聯測量  
- 數據的分層  
- 線性回歸  
第 5 章:假設檢定  
- T 檢定  
- 虛無假設與替代假設  
- P 值  
- 確定樣本大小  
第 6 章:使用統計方法進行數據壓縮  
- 測量尺度  
- 計算示例數據的分佈  
- 計算 Huffman 樹  
- 將原始數據編碼為更小的包裝  
 
 
    
 
    
 
    
 
    
 
    
 
     
    
 
    
 
    