Statistical Analysis with Swift: Data Sets, Statistical Models, and Predictions on Apple Platforms (使用 Swift 進行統計分析:數據集、統計模型與蘋果平台上的預測)
Andersson, Jimmy
- 出版商: Apress
- 出版日期: 2021-10-31
- 定價: $1,980
- 售價: 8.0 折 $1,584
- 語言: 英文
- 頁數: 230
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484277643
- ISBN-13: 9781484277645
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相關分類:
Apple Developer
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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)
第一章:Swift入門
- 介紹Swift及其在處理大型數據集時的優勢
- 提供的數據集以及如何使用Decodable協議加載它們
- 高階函數(map、filter、reduce、apply)
第二章:概率和隨機變量入門
- 什麼是隨機變量?
- 樣本空間
- 概率的法則和公理
- 變量獨立性
- 條件概率
第三章:分佈和隨機數
- 質量和密度函數
- 離散分佈
- 離散均勻分佈
- 伯努利試驗
- 二項分佈
- 泊松分佈
- 連續分佈
- 連續均勻分佈
- 指數分佈
- 正態分佈
- 實現從給定分佈中抽樣的隨機數生成器
第四章:使用線性回歸預測房屋售價
- 中心趨勢測量
- 方差測量
- 相關性測量
- 數據分層
- 線性回歸
第五章:假設檢驗
- T檢驗
- 零假設和對立假設
- P值
- 確定樣本大小
第六章: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
- 將原始數據編碼為較小的包裹