numpy as np

Data from the given table

tv_hours = [70, 43, 71, 36, 86, 61, 79, 24, 52, 40, 70, 32, 74, 37, 80, 40, 83, 26, 89, 32, 63, 80, 96, 15]

Calculating range and standard deviation

range_tv = max(tv_hours) - min(tv_hours) std_dev_tv = round(np.std(tv_hours, ddof=1), 2)

range_tv, std_dev_tvThe range and standard deviation for the data are as follows:

• Range = 81
• Standard Deviation = 23.94

Would you like a detailed explanation of how these values were calculated? Here are some related questions you might find interesting:

1. How does the standard deviation help in understanding the data distribution?
2. What is the difference between population standard deviation and sample standard deviation?
3. How do you calculate variance from standard deviation?
4. What are the advantages of using range vs. standard deviation for measuring spread?
5. How can the range or standard deviation be influenced by outliers?

Tip: When interpreting standard deviation, a larger value indicates that the data points are more spread out from the mean, whereas a smaller value suggests they are closer to the mean.