Using Statsmodes ANOVA in Python

Before using the Statsmodes library for variance analysis, the following preparations need to be made: 1. Install Statsmodes library: Use the 'pip install Statsmodes' command or other appropriate methods to install the Statsmodes library. 2. Import required class libraries: Import the statsmoodels.api module and other required class libraries. Here is an example of using Statsmodes for variance analysis: python import numpy as np import statsmodels.api as sm from statsmodels.formula.api import ols #Prepare sample data data = sm.datasets.get_rdataset('mtcars').data Data ['cyl ']=data ['cyl']. asttype (str) # Change the data type of the cyl column to a string type #Perform analysis of variance model = ols('mpg ~ cyl', data=data).fit() anova_table = sm.stats.anova_lm(model) #Output analysis of variance results print(anova_table) In the above code, we first imported the numpy and statsmoodels.api libraries, and used 'from statsmoodels.formula.api import ols' to import the ols class. Then, we use 'sm. datasets. get'_ Rdataset ('mtcars'). data ` Get the mtcars data set and convert the data Type conversion of the cyl column to the string type. Next, we created a linear regression model using 'ols ('mpg~cyl', data=data). fit() ', where' mpg~cyl 'represents modeling mpg through cyl. Then, we use the fit () method to fit the model and save the results in the model variable. Finally, we use ` sm. stats. anova_ Lm (model) 'Perform analysis of variance and save the results in anova_ In the table variable. Finally, we printed anova_ Table, which is the result of analysis of variance. In this example, we used the mtcars dataset built into Statsmodes. You can download the mtcars dataset from the following link: https://vincentarelbundock.github.io/Rdatasets/csv/datasets/mtcars.csv In practical use, you can choose a suitable dataset for variance analysis based on your own needs.