[USER (data scientist)]: Thank you, Tapilot. Now let's move on. We want to determine if there is a significant correlation between surface types (hard, clay, grass) and player performance (win/loss ratios, player rankings) in the atp_tennis dataset. To do this, we'll create a Correlation Matrix and perform Statistical Tests to obtain p-values and confidence intervals.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import numpy as np
import pandas as pd
import scipy.stats as stats
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print("Correlation Matrix:\n", corr_matrix)

# save data
pickle.dump(corr_matrix,open("./pred_result/corr_matrix.pkl","wb"))
print("P-value:", anova_result_clean.pvalue)

# save data
pickle.dump(anova_result_clean.pvalue,open("./pred_result/pvalue.pkl","wb"))
print("Confidence Intervals:\n", t_critical)

# save data
pickle.dump(t_critical,open("./pred_result/t_critical.pkl","wb"))
print("Hard SurnConfidenceface:", hard_ci)

# save data
pickle.dump(hard_ci,open("./pred_result/hard_ci.pkl","wb"))
print("Clay Surface:", clay_ci)

# save data
pickle.dump(clay_ci,open("./pred_result/clay_ci.pkl","wb"))
print("Grass Surface:", grass_ci)

# save data
pickle.dump(grass_ci,open("./pred_result/grass_ci.pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: Sure, let's create a Correlation Matrix and perform Statistical Tests for the atp_tennis dataset. Here's the code to generate the matrix and perform the tests:
'''
import numpy as np
import pandas as pd
import scipy.stats as stats
import pickle
from decision_company import read_csv_file,

# please import the necessary private functions from decision_company first

atp_tennis = read_csv_file('atp_tennis.csv')

# YOUR SOLUTION BEGIN:
