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Course Notes: Statistical Simulation in Python
Course Notes
Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets
folder.
# Import any packages you want to use here
Take Notes
Add notes here about the concepts you've learned and code cells with code you want to keep.
Add your notes here
95% CONFIDENCE INTERVAL FOR R^2
# Add your code snippets here
rsquared_boot, coefs_boot, sims = [], [], 1000
reg_fit = sm.OLS(df['y'], df.iloc[:,1:]).fit()
# Run 1K iterations
for i in range(sims):
# First create a bootstrap sample with replacement with n=df.shape[0]
bootstrap = df.sample(n=df.shape[0], replace=True)
# Fit the regression and append the r square to rsquared_boot
rsquared_boot.append(sm.OLS(bootstrap['y'],bootstrap.iloc[:,1:]).fit().rsquared)
# Calculate 95% CI on rsquared_boot
r_sq_95_ci = np.percentile(rsquared_boot, [2.5, 97.5])
print("R Squared 95% CI = {}".format(r_sq_95_ci))