- Mastering Python for Data Science
- Samir Madhavan
- 229字
- 2021-07-16 20:14:18
Chapter 2. Inferential Statistics
Before getting understanding the inferential statistics, let's look at what descriptive statistics is about.
Descriptive statistics is a term given to data analysis that summarizes data in a meaningful way such that patterns emerge from it. It is a simple way to describe data, but it does not help us to reach a conclusion on the hypothesis that we have made. Let's say you have collected the height of 1,000 people living in Hong Kong. The mean of their height would be descriptive statistics, but their mean height does not indicate that it's the average height of whole of Hong Kong. Here, inferential statistics will help us in determining what the average height of whole of Hong Kong would be, which is described in depth in this chapter.
Inferential statistics is all about describing the larger picture of the analysis with a limited set of data and deriving conclusions from it.
In this chapter, we will cover the following topics:
- The different kinds of distributions
- Different statistical tests that can be utilized to test a hypothesis
- How to make inferences about the population of a sample from the data given
- Different kinds of errors that can occur during hypothesis testing
- Defining the confidence interval at which the population mean lies
- The significance of p-value and how it can be utilized to interpret results
- Microsoft Exchange Server PowerShell Cookbook(Third Edition)
- C++程序設計(第3版)
- GraphQL學習指南
- Learning Selenium Testing Tools with Python
- vSphere High Performance Cookbook
- Scala Design Patterns
- Mastering Predictive Analytics with Python
- C#實踐教程(第2版)
- Android玩家必備
- Python Data Structures and Algorithms
- Internet of Things with ESP8266
- Vue.js 2 Web Development Projects
- 鴻蒙OS應用編程實戰
- Android高級開發實戰:UI、NDK與安全
- 寫給青少年的人工智能(Python版·微課視頻版)