[USER (data scientist)]: Lastly, I want to do a linear regression for each surface type to see the trends. Please generate a series of values representing the slope and p-value from a linear regression analysis of win/loss ratios over years for each surface type in the ATP tennis dataset.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

import pandas as pd
import scipy.stats as stats
import pickle

atp_tennis = pd.read_csv("atp_tennis.csv") 

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

# Print the trend analysis results for each surface type 
print("Trend Analysis Results:") 
for surface, result in trend_results.items(): 
 print(f"{surface}: Slope = {result['Slope']}, P-value = {result['P-value']}") 
 
# save data
pickle.dump(result['Slope'],open("./ref_result/Slope_" + surface + ".pkl","wb"))
 
# save data
pickle.dump(result['P-value'],open("./ref_result/P-value" + surface + ".pkl","wb"))

---END CODE TEMPLATE---

[YOU (AI assistant)]: You got it! Here's how you can perform the linear regression and print the trend analysis results: 
'''
import pandas as pd
import scipy.stats as stats
import pickle

atp_tennis = pd.read_csv("atp_tennis.csv") 

# YOUR SOLUTION BEGIN:
