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$1,584
  • 語言: 英文
  • 頁數: 230
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484277643
  • ISBN-13: 9781484277645
  • 相關分類: Apple Developer
  • 立即出貨 (庫存 < 3)

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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