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Use the link in the Jupyter Notebook activity to access your Python scriipt. Once you have made your calculations, complete this discussion. The scriipt will output answers to the questions given below. You must attach your Python scriipt output as an HTML file and respond to the questions below.
In this discussion, you will apply the central limit theorem and use principles of the Normal distribution to calculate probabilities. You will demonstrate two key parts of the central limit theorem:
The distribution of sample means is approximately Normally distributed (bell-shaped) as the sample size increases and we repeatedly draw these samples, regardless of the shape of the population distribution from which the samples are drawn.
The average of all sample means is equal to the population mean. In practice, the average of all sample means will closely approximate the population mean.
You will generate a population data set representing total precipitation (TPCP) in tenths of a millimeter using Pythons numpy module. The distribution of this data set will be skewed. This data set will be unique to each student, and therefore the answers will be unique as well. Run Step 1 in the Python scripts to generate your unique population data.