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Central Limit Theorem and use Principles of the Normal Distribution Paper
Use the link in the Jupyter Notebook activity to access your Python script. Once you have made your calculations, complete ...
Central Limit Theorem and use Principles of the Normal Distribution Paper
Use 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 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 Python’s 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 script to generate your unique population data.In your initial post, address the following items: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.What is the mean of the TPCP population data? See Step 3 in the Python script.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.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.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.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.Recall that your distribution of 1,000 means is Normally distributed even though the population distribution was skewed and the grand mean closely approximates the population mean. In your follow-up posts to other students, review your peers' results and provide some analysis and interpretation.Is their population distribution skewed? Is their distribution of 1,000 sample means approximately Normally distributed? Does this confirm the first part of the central limit theorem? Why or why not?Does their grand mean closely approximate their population mean? Does this confirm the second part of the central limit theorem? Why or why not?Based on this discussion activity, what have you learned about the central limit theorem?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.
amdm probability
rest of questions attached Question 4Use the graphic from the previous question to answer the following:Because this is a ...
amdm probability
rest of questions attached Question 4Use the graphic from the previous question to answer the following:Because this is a required course at his school, Mr. Garza's classes filled up before Zane told his counselor which time of day he prefered. The school decides to add an additional teacher, Ms. Lopez, who will teach the computer class 1st and 6th periods. Does this affect the probability of getting Mr. Nelson in the morning? Be very specific in explaining any and all affects this has on the probability.
STA 3215 Rasmussen College Normal Distribution Questions
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7-2 Final Project Milestone Two: Statistical Tools and Data Analysis
InstructionsSubmit a paper and a spreadsheet that provides a justification of the appropriate statistical tools needed to ...
7-2 Final Project Milestone Two: Statistical Tools and Data Analysis
InstructionsSubmit a paper and a spreadsheet that provides a justification of the appropriate statistical tools needed to analyze the company's data, a hypothesis, the results of your analysis, any inferences from your hypothesis test, and a forecasting model that addresses the company's problem.For additional details, please refer to the Milestone Two Guidelines and Rubric document.
Central Limit Theorem and use Principles of the Normal Distribution Paper
Use the link in the Jupyter Notebook activity to access your Python script. Once you have made your calculations, complete ...
Central Limit Theorem and use Principles of the Normal Distribution Paper
Use 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 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 Python’s 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 script to generate your unique population data.In your initial post, address the following items: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.What is the mean of the TPCP population data? See Step 3 in the Python script.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.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.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.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.Recall that your distribution of 1,000 means is Normally distributed even though the population distribution was skewed and the grand mean closely approximates the population mean. In your follow-up posts to other students, review your peers' results and provide some analysis and interpretation.Is their population distribution skewed? Is their distribution of 1,000 sample means approximately Normally distributed? Does this confirm the first part of the central limit theorem? Why or why not?Does their grand mean closely approximate their population mean? Does this confirm the second part of the central limit theorem? Why or why not?Based on this discussion activity, what have you learned about the central limit theorem?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.
amdm probability
rest of questions attached Question 4Use the graphic from the previous question to answer the following:Because this is a ...
amdm probability
rest of questions attached Question 4Use the graphic from the previous question to answer the following:Because this is a required course at his school, Mr. Garza's classes filled up before Zane told his counselor which time of day he prefered. The school decides to add an additional teacher, Ms. Lopez, who will teach the computer class 1st and 6th periods. Does this affect the probability of getting Mr. Nelson in the morning? Be very specific in explaining any and all affects this has on the probability.
STA 3215 Rasmussen College Normal Distribution Questions
CompetencyDemonstrate the use of the normal distribution, the standard normal distribution, and the central limit theorem ...
STA 3215 Rasmussen College Normal Distribution Questions
CompetencyDemonstrate the use of the normal distribution, the standard normal distribution, and the central limit theorem for calculating areas under the normal curve and exploring these concepts in real life applications.ScenarioFrank has only had a brief introduction to statistics when he was in high school 12 years ago, and that did not cover inferential statistics. He is not confident in his ability to answer some of the problems posed in the course.As Frank's tutor, you need to provide Frank with guidance and instruction on a worksheet he has partially filled out. Your job is to help him understand and comprehend the material. You should not simply be providing him with an answer as this will not help when it comes time to take the test. Instead, you will be providing a step-by-step breakdown of the problems including an explanation on why you did each step and using proper terminology.What to SubmitTo complete this assignment, you must first complete the attached word document, and then complete it by including the following items on the worksheet:Incorrect AnswersCorrect any wrong answers. You must also explain the error performed in the problem in your own words.Partially Finished WorkComplete any partially completed work. Make sure to provide step-by-step instructions including explanations.Blank QuestionsShow how to complete any blank questions by providing step-by-step instructions including explanations.Your step-by-step breakdown of the problems, including explanations, should be present within the word document provided. You must also include an Excel workbook which shows all your calculations performed.
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Central Tendency And Variability
The data file that is included in this analysis is Afrobarometer dataset. The categorical and continuous variables from th ...
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