1. In the Python script, you created a histogram for the dataset generated in Step 1. Check to make sure that this data distribution is skewed and included in your attachment. See Step 2 in the Python script.
2. What is the mean of the TPCP population data? See Step 3 in the Python script.
3. In the Python script, you selected a random sample with replacement, of size 50 (note that this is a sufficiently large sample), from the TPCP population. What is the mean of your random sample? Does this sample mean closely approximate the TPCP population mean? See Step 4 in the Python script.
4. You also selected 1,000 random samples of size 50 and calculated the mean of each sample. Then you stored those means into a dataframe. Check to make sure the output of this step is in your attachment. See Step 5 in the Python script.
5. Review the plotted data distribution for these 1,000 means. Does this approximate a Normal distribution? Does this confirm the first part of the central limit theorem? Why or why not? See Step 6 in the Python script.
6. What is the "grand" mean and standard deviation of these 1,000 means? Does the grand mean closely approximate (on a relative basis) the mean of the original distribution? Does this confirm the second part of the central limit theorem? Why or why not?

The data is below

1. TPCP data frame TPCP 0 136.0 1 898.0 2 1310.0 3 408.0 4 479.0 .. ... 495 449.0 496 131.0 497 502.0 498 475.0 499 457.0

Population mean = 483.68

Sample mean = 495.0

Dataframe of 1000 sample means means

0 479.141 450.242 486.163 452.604 475.04.. ...995 478.74996 478.24997 543.88998 499.26999 438.32[1000 rows x 1 columns]