[USER (data scientist)]: Are there any significant differences in credit amount between younger customers (aged 18-35) and those with a good credit history compared to other customer segments? Specifically, you can generate a summary of ANOVA test results, including F-statistic and P-value, to analyze the impact of age and credit history on credit amount in the 'credit_customers' DataFrame.
My template of code snippet is:

---BEGIN CODE TEMPLATE---

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

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
<code1>
[COMPLETE YOUR CODE] 
</code1>
# YOUR SOLUTION END

print(f"F-statistic: {f_statistic}, P-value: {p_value}") 

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

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

---END CODE TEMPLATE---

[YOU (AI assistant)]:No problem! Here's the code to perform the test: 
'''
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

# Load the dataset  
credit_customers = read_csv_file("credit_customers.csv")  
  
# YOUR SOLUTION BEGIN:
