"Total Catalogue"--probability theory and mathematical statistics and python implementation

Note : This is a task spanning several years, and the title can also be called "learning statistics from the To Do list." When I was distressed by P-values a few years ago, I didn't know what Python was, and then, after touching Python, I liked the language. Statistics as the basis of data science, want to do this work, this is always a way around the sill.

In fact, from the middle school began to study statistics, the earliest write "positive" character recount (equivalent to find the majority), is a statistical analysis of the process. There is also a histogram, averaging, find the median. I did not have a complete systematic study of probability theory and mathematical statistics in the school, until in the work, only from the initial impression, gradually the subject and the whole of mathematics separated. Since recognizing the importance of this discipline in their own work (data analysis), it has not taken much time to learn in this area. From the meaning of the initial P-value, to various distributions, hypothesis tests, variance analysis ... Some concepts have seen many times, but still do not understand thoroughly, some have seen, for a long time, and forget. In short, this way, it is really rugged bumpy. Therefore, it is intended to make a summary of the probability theory and mathematical statistics that I have studied recently, and also to be an account of myself. Put a directory here, and the back will be updated constantly. Look forward to interacting with friends who like Python and data analysis to learn from each other.

This summary mainly includes the following aspects:

• Basic concepts;
• implementation of Python;
• Some of the more classic examples.

probability theory

the basic concepts in probability theory

Summary of random variables

One- dimensional discrete random variable and its Python implementation

One- dimensional continuous random variables and their Python implementations

the numerical characteristics of random variables

Mathematical Statistics

The law of large numbers and the central limit theorem

the basic concepts in statistics

three large sampling distributions

09. Parameter Estimation

10. Parameter hypothesis test

11. Goodness of Fit test (non-parametric hypothesis test)

12 Variance Analysis

13. Regression analysis

Supplements

A1. full probability formula and Bayesian formula

References

China University MOOC: Zhejiang University, probability theory and mathematical statistics

China University MOOC: Harbin Institute of Technology, probability theory and mathematical statistics

Https://docs.scipy.org/doc/scipy/reference/stats.html

"Probability theory and Mathematical Statistics", Chen Shiyin, China University of Science and Technology press

"Total Catalogue"--probability theory and mathematical statistics and python implementation