Hi I don't have phyton. I need your help to please run the following code and send both the code and output in picture form please :)

 

import numpy as np

from scipy.stats import t

 

# Sample data

sales_data = np.array([182.11, 1628.82, 782.94, 255.97, 142.80, 14.88, 533.94, 11.18, 8.82, 64.20, 399.98, 6.67, 20.77, 2.33, 15.26, 4.93, 12.84, 113.57, 321.57, 7.30, 247.80, 7.41, 185.38, 12.26, 22.78, 35.91, 19.97, 129.39, 59.81, 22.00, 38.22, 54.50, 61.78, 695.70, 42.80, 872.32, 25.34, 12.96, 89.86, 83.92, 283.92, 31.86, 24.05, 474.95, 14.04, 1.91, 19.14, 271.99, 19.99, 368.97, 156.37, 182.72, 197.37, 95.94, 3.30, 32.40, 340.92, 13.96, 225.30, 971.50, 43.04, 957.58, 32.90, 106.32, 25.06, 1618.37, 35.00, 67.84, 25.92, 1793.98, 12.60, 35.00, 48.69, 15.92, 33,29, 50.35, 60.45, 509.49, 5.23, 826.00, 148.48, 2.95, 13.12, 94.85, 17.64, 998.85, 541.24, 11.56, 99.30, 31.10, 58.24, 442.40, 52.59, 12.96, 5,18, 129.92, 33.11, 1199.98, 10.02, 25.92,180.66, 22.58])

 

# Calculate sample mean and standard deviation

sample_mean = np.mean(sales_data)

sample_std_dev = np.std(sales_data, ddof=1) # ddof=1 for sample standard deviation

 

# Calculate t-value for 95% confidence interval

t_value = t.ppf(0.975, df=len(sales_data)-1)

 

# Calculate margin of error

margin_of_error = t_value * (sample_std_dev / np.sqrt(len(sales_data)))

 

# Calculate confidence interval

lower_bound = sample_mean - margin_of_error

upper_bound = sample_mean + margin_of_error

 

print(f"We are 95% confident that the average sales for the Consumer sector is between ${lower_bound:.2f} and ${upper_bound:.2f}")

print(f"Sample Mean: ${sample_mean:.2f}, Margin of Error: ${margin_of_error:.2f}")