Exploring Saint Thomas Aquinas' Insights on Women's Social Support
Description
This Python code generates simulated data to explore the relationship between emotional intelligence, resilience, and the perceived "Glory in Living." Here's a detailed description of the code: Simulated Data Generation: The code starts by setting a random seed for reproducibility. It generates a time series with 100 time points ranging from 0 to 1. Components of emotional intelligence (sensitivity, understanding, effectiveness) are simulated with random values. Emotional intelligence is then calculated as a weighted sum of these components. Simulating Falls and Total Opportunities: The code simulates the occurrence of falls and total opportunities with random integers. Resilience is calculated as the complement of falls divided by total opportunities. Calculating Glory in Living: Glory in living is calculated as the product of emotional intelligence and resilience. Creating a DataFrame: A pandas DataFrame is created to organize and analyze the generated data, including time, emotional intelligence, resilience, and glory in living. Visualizing the Data: The seaborn pair plot is used to visualize relationships between different variables, providing a quick overview of correlations and distributions. Statistical Analysis: The code calculates the correlation matrix to quantify linear relationships between variables. Linear regression is performed to predict glory in living based on emotional intelligence and resilience. The R-squared value is printed, indicating the proportion of variance in glory in living explained by the linear regression model. Output: The correlation matrix and the R-squared value are printed to provide insights into the strength and direction of relationships between variables.
Files
Steps to reproduce
import matplotlib.pyplot as plt import numpy as np import seaborn as sns import pandas as pd from sklearn.linear_model import LinearRegression # Simulated data np.random.seed(42) time_points = np.linspace(0, 1, 100) # Components of emotional intelligence sensitivity = np.random.uniform(0.5, 1, 100) understanding = np.random.uniform(0.5, 1, 100) effectiveness = np.random.uniform(0.5, 1, 100) # Calculating emotional intelligence emotional_intelligence = 0.4 * sensitivity + 0.4 * understanding + 0.2 * effectiveness # Simulating falls and total opportunities falls = np.random.randint(0, 10, 100) total_opportunities = np.random.randint(10, 20, 100) # Calculating resilience resilience = 1 - falls / total_opportunities # Calculating glory in living glory_in_living = emotional_intelligence * resilience # Creating a DataFrame for analysis data = pd.DataFrame({'Time': time_points, 'Emotional Intelligence': emotional_intelligence, 'Resilience': resilience, 'Glory in Living': glory_in_living}) # Visualizing the data sns.pairplot(data) plt.show() # Statistical analysis correlation_matrix = data.corr() # Linear regression for glory in living prediction X = data[['Emotional Intelligence', 'Resilience']] y = data['Glory in Living'] model = LinearRegression().fit(X, y) r_squared = model.score(X, y) print("\nCorrelation Matrix:") print(correlation_matrix) print("\nLinear Regression R-squared:", r_squared)