Evaluating BMI outcomes

What is the difference in the predicted disease progression (the response, y) for patients who are in the top 10% of BMI compared to the lowest 10% of BMI? You'll use the results of a simulation sampling the multivariate normal distribution to answer this question!

The simulation has already been performed for you: your task is to evaluate the simulation results in df_results.

The following libraries have been imported for you: pandas as pd, numpy as np, and scipy.stats as st.

Cet exercice fait partie du cours

Monte Carlo Simulations in Python

Afficher le cours

Instructions

Use np.quantile() to calculate the 10th and 90th quantile of bmi in the simulated results, saving as bmi_q10 and bmi_q90.
Use bmi_q10 and bmi_q90 to filter df_summary and obtain predicted y values.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

simulation_results = st.multivariate_normal.rvs(mean=mean_dia, size=20000, cov=cov_dia)
df_results = pd.DataFrame(simulation_results,columns=["age", "bmi", "bp", "tc", "ldl", "hdl", "tch", "ltg", "glu"])
predicted_y = regr_model.predict(df_results)
df_y = pd.DataFrame(predicted_y, columns=["predicted_y"])
df_summary = pd.concat([df_results,df_y], axis=1)

# Calculate the 10th and 90th quantile of bmi in the simulated results
bmi_q10 = np.quantile(df_summary["bmi"], ____)
bmi_q90 = np.quantile(df_summary["bmi"], ____)

# Use bmi_q10 and bmi_q90 to filter df_summary and obtain predicted y values
mean_bmi_q90_outcome = np.mean(df_summary[____]["predicted_y"]) 
mean_bmi_q10_outcome = np.mean(df_summary[____]["predicted_y"])
y_diff = mean_bmi_q90_outcome - mean_bmi_q10_outcome
print(y_diff)

Modifier et exécuter le code