Demonstrate

Steps to Complete Week 5 Lab

Use the Weeks 3 and 5 spreadsheets from the Weeks 3 and 5 Lessons to help you answer the questions below.

Step 1: Your instructor will provide you with 10 values to use for this lab.

Gather 10 MORE of your own to add to the 10 provided by your instructor. Do the following:

Survey or measure 10 people to find their heights. Determine the mean and standard deviation for the 20 values by using the Week 3 Excel spreadsheet. Post a screen shot of the portion of the spreadsheet that helped you determine these values. How does your height compare to the mean (average) height of the 20 values? Is your height taller, shorter, or the same as the mean of the sample?

Note: The following image is just an example. They are NOT the values you should be using for your lab. Your instructor should have sent you our data values for your Week 5 Lab. Please reach out to your instructor if you do not have your data values.

Data Example of 10 people with different heights

Data Example of 10 people with different heights

(your spreadsheet will have 20 values—10 from your instructor and 10 from your own data gathering).

Step 2: Give some background information on the group of people you used in your study. You might consider using the following questions to guide your answer.

How did you choose the participants for your study? What was the sampling method: systematic, convenience, cluster, stratified, simple random?

What part of the country did your study take place in?

What are the age ranges of your participants?

How many of each gender did you have in your study?

What are other interesting factors about your group?

Step 3: Use the Week 5 Excel spreadsheet for the following.

(Use the Empirical Rule tab from the spreadsheet). Determine the 68%, 95%, and 99.7% values of the Empirical Rule in terms of the 20 heights in your height study.

What do these values tell you?

Post a screen shot of your work from the Week 5 Excel spreadsheet.

Week 5 Spreadsheet Example

(Use the normal probability tab from the spreadsheet). Based on your study results, what percent of the study participants are shorter than you? What percent are taller than you?

Post a screen shot of your work from the Week 5 Excel spreadsheet.

Example: If my height is 73 inches, then 20.86% of the relevant population is shorter. The other 79.14%, of course, is taller.

Week 5 Spreadsheet Example

Step 4: Be sure your name is on the Word document, save it, and then submit it under “Assignments” and “Week 5: Lab”.

*Word document is attached that needs to be filled put for the lab.

# Category: Statistics

Independent Research Project – In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment. In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment.

In the Independent Research Project (IRP), students conduct an analysis project using SAS primarily and Excel as needed to analyze real-world data. Note: You are required to do a ANOVA Analysis or a Regression Analysis and submit them showing Pr or P values, R-square, B values, Plots, etc. – as needed for the type of analysis you are conducting (ANOVA or Regression). Homogeneity of Variance of residuals can be assumed so you can proceed with your required analysis.

Demonstrate

Steps to Complete Week 5 Lab

Use the Weeks 3 and 5 spreadsheets from the Weeks 3 and 5 Lessons to help you answer the questions below.

Step 1: Your instructor will provide you with 10 values to use for this lab.

Gather 10 MORE of your own to add to the 10 provided by your instructor. Do the following:

Survey or measure 10 people to find their heights. Determine the mean and standard deviation for the 20 values by using the Week 3 Excel spreadsheet. Post a screen shot of the portion of the spreadsheet that helped you determine these values. How does your height compare to the mean (average) height of the 20 values? Is your height taller, shorter, or the same as the mean of the sample?

Note: The following image is just an example. They are NOT the values you should be using for your lab. Your instructor should have sent you our data values for your Week 5 Lab. Please reach out to your instructor if you do not have your data values.

Data Example of 10 people with different heights

Data Example of 10 people with different heights

(your spreadsheet will have 20 values—10 from your instructor and 10 from your own data gathering).

Step 2: Give some background information on the group of people you used in your study. You might consider using the following questions to guide your answer.

How did you choose the participants for your study? What was the sampling method: systematic, convenience, cluster, stratified, simple random?

What part of the country did your study take place in?

What are the age ranges of your participants?

How many of each gender did you have in your study?

What are other interesting factors about your group?

Step 3: Use the Week 5 Excel spreadsheet for the following.

(Use the Empirical Rule tab from the spreadsheet). Determine the 68%, 95%, and 99.7% values of the Empirical Rule in terms of the 20 heights in your height study.

What do these values tell you?

Post a screen shot of your work from the Week 5 Excel spreadsheet.

Week 5 Spreadsheet Example

(Use the normal probability tab from the spreadsheet). Based on your study results, what percent of the study participants are shorter than you? What percent are taller than you?

Post a screen shot of your work from the Week 5 Excel spreadsheet.

Example: If my height is 73 inches, then 20.86% of the relevant population is shorter. The other 79.14%, of course, is taller.

Week 5 Spreadsheet Example

Step 4: Be sure your name is on the Word document, save it, and then submit it under “Assignments” and “Week 5: Lab”.

*Word document is attached that needs to be filled put for the lab.

Independent Research Project – In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment. In this assignment, you will create and submit a business memo (template attached) that clearly communicates your analysis of the data set you identified in the previous assignment.

In the Independent Research Project (IRP), students conduct an analysis project using SAS primarily and Excel as needed to analyze real-world data. Note: You are required to do a ANOVA Analysis or a Regression Analysis and submit them showing Pr or P values, R-square, B values, Plots, etc. – as needed for the type of analysis you are conducting (ANOVA or Regression). Homogeneity of Variance of residuals can be assumed so you can proceed with your required analysis.

Hi, Please write a research proposal on any topic related to environmental issues.

**In this proposal, please use methodology 1. Quasi-experimental methods: Difference-differences. OR. 2. Quasi-experimental methods: Regression discontinuity design.

You should include 1. TITLE. 2. BACKGROUND AND RATIONALE. 3. RESEARCH QUESTION(S). 4. RESEARCH METHODOLOGY 5. Hypothesis

You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriiptive statistics and graphs provided.

Directions

Using the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate Data Spreadsheet spreadsheet (both found in the Supporting Materials section) for your statistical analysis.

Note: Present your data in a clearly labeled table and using clearly labeled graphs.

Specifically, include the following in your report:

Introduction

Describe the report: Give a brief descriiption of the purpose of your report.

Define the question your report is trying to answer.

Explain when using linear regression is most appropriate.

When using linear regression, what would you expect the scatterplot to look like?

Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variables.

Data Collection

Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).

Identify your predictor and response variables.

Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.

Data Analysis

Histogram: Create a histogram for each of the two variables.

Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.

Interpret the graphs and statistics:

Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.

Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?

Develop Your Regression Model

Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.

Based on your scatterplot, explain if a regression model is appropriate.

Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.

Identify any possible outliers or influential points and discuss their effect on the correlation.

Discuss keeping or removing outlier data points and what impact your decision would have on your model.

Find r: Find the correlation coefficient (r).

Explain how the r value you calculated supports what you noticed in your scatterplot.

Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.

Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.

Interpret regression equation: Interpret the slope and intercept in context.

Strength of the equation: Provide and interpret R-squared.

Determine the strength of the linear regression equation you developed.

Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.

Conclusions

Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.

Did you see the results you expected, or was anything different from your expectations or experiences?

What changes could support different results, or help to solve a different problem?

Provide at least one question that would be interesting for follow-up research.

Hello, please edit the answer according to the hints on the home page when you are doing the question. The screenshot is in the correct format as requested by my professor. Most of the answers can be found online, such as Chegg. But I want the questions that require text answers to be written in the correct form that my professor requires.

Use the Word document below and follow the instruction carefully to complete the assignment. Use this document to type in your answers and show the work/process/rationale supporting the answers. The Excel document contains the data sets that are also included in the Word document.

You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriiptive statistics and graphs provided.

Directions

Using the Project One Template located in the What to Submit section, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate Data Spreadsheet spreadsheet (both found in the Supporting Materials section) for your statistical analysis.

Note: Present your data in a clearly labeled table and using clearly labeled graphs.

Specifically, include the following in your report:

Introduction

Describe the report: Give a brief descriiption of the purpose of your report.

Define the question your report is trying to answer.

Explain when using linear regression is most appropriate.

When using linear regression, what would you expect the scatterplot to look like?

Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variables.

Data Collection

Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).

Identify your predictor and response variables.

Scatterplot: Create a scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.

Data Analysis

Histogram: Create a histogram for each of the two variables.

Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.

Interpret the graphs and statistics:

Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.

Compare and contrast the shape, center, spread, and any unusual characteristic for your sample of house sales with the national population. Is your sample representative of national housing market sales?

Develop Your Regression Model

Scatterplot: Provide a scatterplot of the variables with a line of best fit and regression equation.

Based on your scatterplot, explain if a regression model is appropriate.

Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.

Identify any possible outliers or influential points and discuss their effect on the correlation.

Discuss keeping or removing outlier data points and what impact your decision would have on your model.

Find r: Find the correlation coefficient (r).

Explain how the r value you calculated supports what you noticed in your scatterplot.

Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equation. Assess the strength of the model.

Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.

Interpret regression equation: Interpret the slope and intercept in context.

Strength of the equation: Provide and interpret R-squared.

Determine the strength of the linear regression equation you developed.

Use regression equation to make predictions: Use your regression equation to predict how much you should list your home for based on the square footage of your home.

Conclusions

Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.

Did you see the results you expected, or was anything different from your expectations or experiences?

What changes could support different results, or help to solve a different problem?

Provide at least one question that would be interesting for follow-up research.

## C) multiple regression

The Company we have selected to do our stock analysis is Zillow Inc. (Ticker: ZG)

You may want to gather data (preferably quarterly) over a long period of time (try for 10 years so that you have roughly at least 40 observations).

Please see “QM 717 Fall 2022 Final Project Instructions” for FULL INSTRUCTIONS:

Make sure in the report that you have explained/done all of the following at least once: (All of the below will need to be run in excel through the “Data Analysis” add-in function and will be required to be included as appendices at the end. Also, include the excel file you used as part of submission.

a) R-squared

b) Standard error

c) Multiple regression

d) Explain in simple language what each coefficient tells us (most important step!)

e) Explain the statistical significance of each coefficient

f) Explain if there are any outliers and if there are, test the effect of eliminating them

g) Use the coefficients and your best guess about next quarter’s (1st quarter) right-hand side values to predict the company’s stock price (or change in stock price)

h) Provide the 95% confidence interval for this predicted stock price (i.e., 95% of the time we expect the stock price on Feb. 1st of next year to be between what two values)