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oai:data.mendeley.com/9bj4hnjtw6.1 2024-05-07T03:39:13Z

Benedict, Sunil Maria Belief in Divine Intervention vs Psychological Resilience Mendeley Data import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt # Step 1: Simulate data np.random.seed(42) n_samples = 120 belief_in_divine = np.random.uniform(1, 10, n_samples) psychological_resilience = belief_in_divine + np.random.normal(0, 1, n_samples) # Adding noise # Create DataFrame experiment_df = pd.DataFrame({'Belief_in_Divine': belief_in_divine, 'Psychological_Resilience': psychological_resilience}) # Step 2: Visualize the relationship with a scatter plot plt.figure(figsize=(8, 6)) sns.scatterplot(x='Belief_in_Divine', y='Psychological_Resilience', data=experiment_df) plt.title('Belief in Divine Intervention vs. Psychological Resilience') plt.xlabel('Belief in Divine Intervention') plt.ylabel('Psychological Resilience') # Step 3: Add regression line equation to the plot plt.text(3, 15, r'$Y = MX + C$', fontsize=12) # Step 4: Generate individual plots for each regression line for i in range(5): # Create a new figure for each line plt.figure(figsize=(8, 6)) # Randomly select slope and intercept values slope = np.random.uniform(0.5, 1.5) intercept = np.random.uniform(-1, 2) # Plot regression line x_values = np.linspace(min(belief_in_divine), max(belief_in_divine), 100) y_values = slope * x_values + intercept plt.plot(x_values, y_values, label=f'Line {i+1}: Y = {slope:.2f}X + {intercept:.2f}') # Add scatter plot on top sns.scatterplot(x='Belief_in_Divine', y='Psychological_Resilience', data=experiment_df) plt.title(f'Regression Line {i+1}: Belief in Divine Intervention vs. Psychological Resilience') plt.xlabel('Belief in Divine Intervention') plt.ylabel('Psychological Resilience') plt.legend() plt.grid(True) plt.show() # Step 5: Correlation Analysis correlation_coefficient = np.corrcoef(experiment_df['Belief_in_Divine'], experiment_df['Psychological_Resilience'])[0, 1] print("Correlation Coefficient:", correlation_coefficient) # Step 6: Perform Linear Regression X = experiment_df['Belief_in_Divine'].values.reshape(-1, 1) y = experiment_df['Psychological_Resilience'].values # Calculate coefficients X = np.column_stack((np.ones_like(X), X)) # Add intercept term coefficients = np.linalg.lstsq(X, y, rcond=None)[0] slope = coefficients[1] intercept = coefficients[0] print("Slope (m):", slope) print("Intercept (c):", intercept) # Step 7: Plot regression line plt.figure(figsize=(8, 6)) plt.scatter(experiment_df['Belief_in_Divine'], experiment_df['Psychological_Resilience']) x_values = experiment_df['Belief_in_Divine'].values # Convert to numpy array plt.plot(x_values, slope * x_values + intercept, color='red', label='Regression Line') plt.title('Regression Analysis: Belief in Divine Intervention vs. Psychological Resilience') plt.xlabel('Belief in Divine Intervention') plt.ylabel('Psychological Resilience') plt.legend() plt.grid(True) plt.show() Psychology Educational Psychology Emotional Development Emotional Intelligence Resilience Dataset doi:10.17632/9bj4hnjtw6.1 oai:data.mendeley.com/9bj4hnjtw6.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/9bj4hnjtw6 2024-05-07T03:39:13Z