Python uses NumPy for statistical analysis, including hypothesis testing, analysis of variance, regression analysis, etc

Preparation work for environmental construction: 1. Install Python: From the official website https://www.python.org/downloads/ Download and install the latest version of Python. 2. Install NumPy: Use the following command to install NumPy on the command line. shell pip install numpy 3. Install other dependent libraries: Install other required dependent libraries as needed, such as Pandas, Scipy, etc. Sample data description: To demonstrate the functionality of statistical analysis, we will use a virtual height and weight dataset containing 1000 samples. Code implementation: python import numpy as np #Height and weight data heights = np.random.normal(170, 10, 1000) weights = np.random.normal(65, 5, 1000) #Hypothesis testing from scipy import stats t_stat, p_value = stats.ttest_ind(heights, weights) print("t-statistic:", t_stat) print("p-value:", p_value) #Analysis of variance from scipy import stats f_stat, p_value = stats.f_oneway(heights, weights) print("F-statistic:", f_stat) print("p-value:", p_value) #Linear regression from scipy import stats slope, intercept, r_value, p_value, std_err = stats.linregress(heights, weights) print("Slope:", slope) print("Intercept:", intercept) print("R-squared:", r_value**2) print("p-value:", p_value) print("Standard Error:", std_err) In the above code, we first used NumPy to generate 1000 height and weight data subject to Normal distribution. Then use the stats module in the scipy library for hypothesis testing (using independent sample t-tests) and analysis of variance (using one-way analysis of variance). Finally, the linear regression analysis is carried out using the linregeress () method of the stats module to calculate the slope, intercept, R-squared value, p-value and Standard error. Please note that the dataset here is virtual, and you can also use other datasets for statistical analysis.