In your initial post, address the following items: 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. TPCP data frame TPCP 0 367.0 575. 2 4 3 2278.0 83.0 495 112.0 496 280.0 497 166.0 498 758.0 99 206.0 [500 rows x 1 columns] TPCP population distribution 50 30 Frequency 20 10 500 1000 1500 2000 TPCP 2. What is the mean of the TPCP population data? See Step 3 in the Python script. Population mean = 461.1 B. 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. Sample mean = 474.76-Yes, it does. 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. Dataframe of 1000 sample means means 0 385.82 1 500.28 2 425.46 3 413.74 530.96 995 466.82 996 464.76 997 433.60 998 490.98 999 445.26 [1000 rows x 1 columns] 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. The distribution is approximately a Normal distribution. It does confirm the first part of the central limit theorem. The central limit theorem states that the sampling distribution of a sample mean is approximately normal if the sample size is large enough, even if the population distribution is not normal. The first part of theorem - the mean of the sampling distribution will be equal to the mean of the population distribution (Zach, 2019). So, as the sample size becomes larger, and assuming that all samples are identical in size, regardless of the population distribution shape, bears this out. 1 ~ [!Update Citations and Bibliography Distribution of 1000 sample means 60 50 40 Frequency 20- 10 300 350 400 450 500 550 600 Means 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? See Step 7 in the Python script. Grand Mean (Mean of 1000 sample means) = 459.53 Std Deviation of 1000 sample means = 49.91 Probability that a specific mean is 450 or less = 0.4243 The "grand" mean means that the mean of all the samples from the same population will be approximately equal to the mean of the population (Jones, 2018). The standard deviation means that the standard deviations will equal the population standard deviation. The "grand" mean is close to the mean of the original distribution, which satisfies the second part of the central limit theorem.The central limit theorem also predicts that standard deviation of the sample means will b? (IA/E. Does the standard deviation that you observed, 49.91, match this prediction