InstructionsData Set 1 presents a sample of annual salaries for recently hired plant operators at a chemical manufacturing company. Use Excel's ToolPak (or any statistical package that you are comfortable with) to compute descriptive statistics for the data. Submit your statistical output from Excel, which should include values for the mean, median, mode, sample variance, and sample standard deviation, and a one-page Word document in which you present an analysis of your results.

PART 1Use the link in the Jupyter Notebook activity to access your Python script. Once you have made your calculations, complete this discussion. The script will output answers to the questions given below. You must attach your Python script output as an HTML file and respond to the questions below.In this discussion, you will apply the statistical concepts and techniques covered in this week's reading about correlation coefficient and simple linear regression. A car rental company wants to evaluate the premise that heavier cars are less fuel efficient than lighter cars. In other words, the company expects that fuel efficiency (miles per gallon) and weight of the car (often measured in thousands of pounds) are correlated. Performing this analysis will help the company optimize its business model and charge its customers appropriately.In this discussion, you will work with a cars data set that includes two variables:Miles per gallon (coded as mpg in the data set)Weight of the car (coded as wt in the data set)The random sample will be drawn from a CSV file. This data will be unique to you, and therefore your answers will be unique as well. Run Step 1 in the Python script to generate your unique sample data.In your initial post, address the following items:You created a scatterplot of miles per gallon against weight; check to make sure it was included in your attachment. Does the graph show any trend? If yes, is the trend what you expected? Why or why not? See Step 2 in the Python script.What is the coefficient of correlation between miles per gallon and weight? What is the sign of the correlation coefficient? Does the coefficient of correlation indicate a strong correlation, weak correlation, or no correlation between the two variables? How do you know? See Step 3 in the Python script.Write the simple linear regression equation for miles per gallon as the response variable and weight as the predictor variable. How might the car rental company use this model? See Step 4 in the Python script.What is the slope coefficient? Is this coefficient significant at a 5% level of significance (alpha=0.05)? (Hint: Check the P-value, , for weight in the Python output.) See Step 4 in the Python script.In your follow-up posts to other students, review your peers' calculations and provide some analysis and interpretation:How do their plots and correlation coefficients compare with yours?Would you recommend this regression model to the car rental company? Why or why not?Remember to attach your Python output and respond to all questions in your initial and follow-up posts. Be sure to clearly communicate your ideas using appropriate terminology. Finally, be sure to review the Discussion Rubric to understand how you will be graded on this assignment.PART 2 For this project, you will submit the Python script you used to make your calculations and a summary report explaining your findings.1.Python Script: To complete the tasks listed below, open the Project Two Jupyter Notebook link in the Assignment Information module. Your project contains the NBA data set and a Jupyter Notebook with your Python scripts. In the notebook, you will find step-by-step instructions and code blocks that will help you complete the following tasks:oHypothesis tests for a population parameter§Hypothesis tests for a population mean§Hypothesis test for a population proportionoHypothesis test for the difference between two population parameters§Hypothesis test for difference between two population means2.Summary Report: Once you have completed all the steps in your Python script, you will create a summary report to present your findings. Use the provided template to create your report. You must complete each of the following sections:oIntroduction: Set the context for your scenario and the analyses you will be performing.oHypothesis tests for the population mean: Discuss all steps of the hypothesis tests and interpret your results.oHypothesis test for the population proportion: Discuss all steps of the hypothesis test and interpret your results.oHypothesis test for the difference between two population means: Discuss all steps of the hypothesis test and interpret your results.oConclusion: Summarize your findings and explain their practical implications.What to SubmitTo complete this project, you must submit the following:Python ScriptYour Jupyter Notebook Python script contains all the statistical analyses you completed for this project. You downloaded your work as an HTML file. Review the file to make sure that every step and all your outputs are included. Submit the HTML file as part of your submission. Review the Jupyter Notebook in Codio Tutorial in the Supporting Materials section if you need help.Summary ReportUse the provided template to create your summary report. The template contains guiding questions to help you complete each section. Be sure to remove these questions before submitting your report. Your summary report should be submitted as a 3- to 5-page Microsoft Word document. It should include an APA-style cover page and APA citations for any sources used. Use double spacing, 12-point Times New Roman font, and one-inch margins.