pandas as pd

Load the provided CSV file to examine the data

file_path = '/mnt/data/173956487615708le2qpo.csv' data = pd.read_csv(file_path)

Display the first few rows of the data to understand its structure

data.head()It looks like the data includes two columns that are labeled "Study" and "Socialize" (with "X" and "Y" representing the names). I'll now clean the data and assign appropriate column names to work with the "Study Hours" as the predictor variable (X) and "Socialize Hours" as the response variable (Y). Let's proceed with this. Clean the data by renaming the columns and removing any unnecessary rows data.columns = ['User', 'Study Hours', 'Socialize Hours'] data = data.drop(0) # Remove the first row, which contains the header

Convert relevant columns to numeric values

data['Study Hours'] = pd.to_numeric(data['Study Hours'], errors='coerce') data['Socialize Hours'] = pd.to_numeric(data['Socialize Hours'], errors='coerce')

Display the cleaned data

data.head()The data has been cleaned up, and we now have "Study Hours" and "Socialize Hours" as the relevant columns.

Next, let's create the scatter plot with "Study Hours" as the predictor variable (X) and "Socialize Hours" as the response variable (Y). We'll also calculate the correlation coefficient, the regression equation, and analyze the proportion of variance accounted for. matplotlib.pyplot as plt import numpy as np from scipy.stats import linregress

Scatter plot

plt.figure(figsize=(8,6)) plt.scatter(data['Study Hours'], data['Socialize Hours'], color='blue', label='Data points') plt.title('Scatter Plot: Study Hours vs. Socialize Hours') plt.xlabel('Study Hours') plt.ylabel('Socialize Hours')

Perform linear regression

slope, intercept, r_value, p_value, std_err = linregress(data['Study Hours'], data['Socialize Hours'])

Plot regression line

x_values = np.linspace(min(data['Study Hours']), max(data['Study Hours']), 100) y_values = slope * x_values + intercept plt.plot(x_values, y_values, color='red', label=f'Regression Line: y = {slope:.2f}x + {intercept:.2f}')

Show plot with legend

plt.legend() plt.grid(True) plt.show()

Calculate the correlation coefficient (r), R-squared (r^2), and the regression equation

r_squared = r_value ** 2 regression_equation = f'y = {slope:.2f}x + {intercept:.2f}'

slope, intercept, r_value, r_squared, regression_equationThe scatter plot with the regression line has been generated. Here are the key results:

• Correlation Coefficient (r): -0.0181 (This indicates a very weak negative correlation between "Study Hours" and "Socialize Hours".)
• Regression Equation: $y = -0.04x + 19.25$ (This is the line that best fits the data.)
• R-squared (r²): 0.00033 (This suggests that only about 0.033% of the variance in "Socialize Hours" is explained by "Study Hours".)
• Proportion of Variance Not Accounted For (1 - r²): 0.99967 (This means that 99.967% of the variance in "Socialize Hours" is unexplained by the "Study Hours".)

Now, let’s compare the standard errors of the residuals (Sy and Sy').

• Sy is the standard deviation of the observed "Socialize Hours" values.
• Sy' is the standard error of the regression (how much the predicted "Socialize Hours" values deviate from the regression line).

Let me calculate and compare these values. Calculate Sy (standard deviation of actual values) sy = np.std(data['Socialize Hours'], ddof=1)

Calculate Sy' (standard error of regression)

sy_pred = std_err

sy, sy_predIt seems like I can’t do more advanced data analysis right now. Please try again later. If you have any other questions or need assistance with another task, feel free to let me know!