Statistik ringkas

Simulasi Monte Carlo di Python

Izzy Weber

Curriculum Manager, DataCamp

Luaran usia

Berapa perbedaan luaran untuk orang pada kuantil usia pertama dan keempat? histogram variabel Age pada hasil simulasi

Luaran usia

print(np.quantile(df_summary["age"], 0.25))
print(np.quantile(df_summary["age"], 0.75))

39.78584463094688
57.52092642086441

Luaran usia

age_q25 = np.quantile(df_summary["age"], 0.25)
age_q75 = np.quantile(df_summary["age"], 0.75)


mean_age_q75_outcome = np.mean(df_summary[df_summary["age"] > age_q75]["predicted_y"]) 
mean_age_q25_outcome = np.mean(df_summary[df_summary["age"] < age_q25]["predicted_y"])


mean_age_q75_outcome - mean_age_q25_outcome

34.09429663553621

Perbedaan luaran berdasarkan usia dan BMI

Berapa perbedaan luaran untuk orang pada kuantil pertama dan keempat BMI dan usia?
Berapa interval kepercayaan 95% dan simpangan bakunya?

Perbedaan luaran berdasarkan usia dan BMI

y_diffs = []
for i in range(1000):
    simulation_results = st.multivariate_normal.rvs(mean=mean_dia, size=1000, 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_sum = pd.concat([df_results, df_y], axis=1)

    age_q25 = np.quantile(df_sum["age"], 0.25)
    age_q75 = np.quantile(df_sum["age"], 0.75)
    bmi_q25 = np.quantile(df_sum["bmi"], 0.25)
    bmi_q75 = np.quantile(df_sum["bmi"], 0.75)

    q75_outcome = np.mean(df_sum[(df_sum["bmi"] > bmi_q75) & (df_sum["age"] > age_q75)]["predicted_y"]) 
    q25_outcome = np.mean(df_sum[(df_sum["bmi"] < bmi_q25) & (df_sum["age"] < age_q25)]["predicted_y"])

    y_diff = q75_outcome - q25_outcome 
    y_diffs.append(y_diff)

Perbedaan luaran berdasarkan usia dan BMI

Rata-rata:
```
np.mean(y_diffs)
```

132.4948511247819

Interval kepercayaan 95%:
```
np.quantile(y_diffs,0.025)
```

120.73340800299707

np.quantile(y_diffs,0.975)

144.1344994507557

Simpangan baku:
```
np.std(y_diffs)
```

5.9322225537128

Histogram hasil:
```
sns.histplot(y_diffs)
```

Ayo berlatih!

Simulasi Monte Carlo di Python