- import numpy as np
- import matplotlib.pyplot as plt
- from scipy.stats import norm
- # Values for z-distribution
- z_values = np.linspace(-5, 5, 100)
- y_values = norm.pdf(z_values)
- # Plotting the standard normal distribution
- plt.figure(figsize=(10, 5))
- plt.plot(z_values, y_values, label='Standard Normal Distribution')
- # Highlight critical regions for two-tailed test
- plt.fill_between(z_values, 0, y_values, where=z_values <= -2.576, color='red', alpha=0.5, label='Critical Region (left tail)')
- plt.fill_between(z_values, 0, y_values, where=z_values >= 2.576, color='red', alpha=0.5, label='Critical Region (right tail)')
- # Marking the test statistic
- plt.axvline(-4.048, color='k', linestyle='dashed', label='Test Statistic z=-4.048')
- plt.title('Z-Distribution with Critical Regions and Test Statistic')
- plt.xlabel('Z-Value')
- plt.ylabel('Probability Density')
- plt.legend()
- plt.show()
[text] tet
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- 02 May-2024
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