Shulou(Shulou.com)06/01 Report--

This article mainly introduces the relevant knowledge of how to use the python variance test method, the content is detailed and easy to understand, the operation is simple and fast, and it has a certain reference value. I believe you will gain something after reading this python variance test method. Let's take a look at it.

Description

1. The test of variance is used to compare the samples of data of two or more variables to determine whether the differences between them are simple and random, or due to significant statistical differences between processes.

2. The independent variable X is a kind of discrete data, and the independent variable Y is a kind of continuous data (x can be of many types). If the data is normally distributed, the variance should be homogeneous.

Example import pandas as pdimport numpy as npfrom statsmodels.formula.api import olsfrom statsmodels.stats.anova import anova_lmdata = pd.DataFrame ([[1,1,32], [1,2,35], [1,3,35.5], [1,4,38.5], [2,1,33.5], [2,3,36.5], [2,3,38], [2,4,39.5], [3,1,36], [3,2,37.5], [3,3] 39.5], [3,4,43], columns= ["x1", "x2", "y"]) # multiple factors without repetition Do not calculate the influence of interaction model = ols ("yearly C (x1) + C (x2)", data=data [["x1", "x2", "y"]]) .fit () anovat = anova_lm (model) anovat

Knowledge points to add:

Analysis of variance can be used to infer whether the level or interaction of one or more factors will have a significant impact on the experimental indicators when their state changes. It is mainly divided into single-factor analysis of variance, multi-factor non-repetitive analysis of variance and multi-factor repeated analysis of variance.

Do mathematical statistics after-class questions, found that the analysis of variance calculation is more troublesome, want to use Python packet switching to achieve. However, it is found that the explanation of the parameters in most tutorials is not very clear, so make a note here.

The main libraries used are pandas and statsmodels. The brief process is to first construct the input data format using the DataFrame data structure of the pandas library. Then the least square linear regression model is obtained by using the ols function in statsmodels library. Finally, the anova_lm function in statsmodels library is used for analysis of variance.

This is the end of import pandas as pdimport numpy as npfrom statsmodels.formula.api import olsfrom statsmodels.stats.anova import anova_lm 's article on "how to use python Variance Test". Thank you for reading! I believe that everyone has a certain understanding of the knowledge of "how to use python variance test". If you want to learn more knowledge, you are welcome to follow the industry information channel.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.