Python uses Statsmodes to perform probability distribution fitting and parameter estimation, including Normal distribution, Poisson distribution, gamma distribution, etc

Environmental construction: 1. Install Statsmodes library: Use the pip command to install the Statsmodes library. pip install statsmodels Dependent class libraries: -Pandas: Used for data processing and conversion. -Numpy: Used for numerical calculations. Dataset: In this example, we will use the dataset 'long' that comes with Statsmodes. This dataset contains US economic data from 1947 to 1962. python import statsmodels.api as sm #Loading the Longley dataset data = sm.datasets.macrodata.load_pandas().data Sample data: `Data 'is a DataFrame that contains multiple variables, with the most important variable being' unemp '(unemployment rate). The complete sample code is as follows: python import numpy as np import pandas as pd import statsmodels.api as sm from scipy.stats import t def fit_normal_distribution(data): mu, std = norm.fit(data) return mu, std def fit_poisson_distribution(data): mu = poisson.fit(data) return mu def fit_gamma_distribution(data): a, loc, scale = gamma.fit(data) return a, loc, scale #Loading the Longley dataset data = sm.datasets.macrodata.load_pandas().data #Select the data to be fitted unemployment_rate = data["unemp"] #Fitting Normal distribution mu, std = fit_normal_distribution(unemployment_rate) #Fitting Poisson distribution mu_poisson = fit_poisson_distribution(unemployment_rate) #Fitting gamma distribution a, loc, scale = fit_gamma_distribution(unemployment_rate) #Print fitting results Print ("Normal distribution:") Print ("mean:", mu) Print ("Standard deviation:", std) print(" ") Print ("Poisson distribution:") Print ("mean:", mu_poisson) print(" ") Print ("Gamma distribution:") Print ("Shape parameters:", a) Print ("positional parameter:", loc) Print ("Scale parameter:", scale) Please ensure that the required libraries are installed before running the code.