Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 1/9 Project One Guidelines and Rubric Competencies In this project, you will demonstrate your mastery of the following competencies: Apply sta±s±cal techniques to address research problems Perform regression analysis to address an authen±c problem Overview The purpose of this project is to have you complete all of the steps of a real-world linear regression research project star±ng with developing a research ques±on, then comple±ng a comprehensive sta±s±cal analysis, and ending with summarizing your research conclusions. Scenario You have been hired by the D. M. Pan Na±onal Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this informa±on to help their real estate agents be²er determine the use of square footage as a benchmark for lis±ng prices on homes. Your task is to provide a report predic±ng the median 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 na±onal descrip±ve sta±s±cs and graphs provided. Direc±ons Using the Project One Template located in the What to Submit sec±on, generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the lis±ng price should be. Reference the Na±onal Sta±s±cs and Graphs document for na±onal comparisons and the Real Estate County Data spreadsheet (both found in the Suppor±ng Materials sec±on) for your sta±s±cal analysis. Note: Present your data in a clearly labeled table and using clearly labeled graphs. Specifically, include the following in your report: Introduc±on A. Describe the report: Give a brief descrip±on of the purpose of your report. a. Define the ques±on your report is trying to answer. b. Explain when using linear regression is most appropriate. i. When using linear regression, what would you expect the sca²erplot to look like? c. Explain the difference between response and predictor variables in a linear regression to jus±fy the selec±on of variables. Data Collec±on A. Sampling the data: Select a random sample of 50 coun±es. a. Iden±fy your response and predictor variables. B. Sca²erplot: Create a sca²erplot of your response and predictor variables to ensure they are appropriate for developing a linear model. Data Analysis   MAT-240-X5769 Applied Statistics 21EW5 CP

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 2/9 A. Histogram: For your two variables, create histograms. B. Summary sta±s±cs: For your two variables, create a table to show the mean, median, and standard devia±on. C. Interpret the graphs and sta±s±cs: a. Based on your graphs and sample sta±s±cs, interpret the center, spread, shape, and any unusual characteris±c (outliers, gaps, etc.) for the two variables. b. Compare and contrast the shape, center, spread, and any unusual characteris±c for your sample of house sales with the na±onal popula±on. Is your sample representa±ve of na±onal housing market sales? Develop Your Regression Model A. Sca²erplot: Provide a graph of the sca²erplot of the data with a line of best fit. a. Explain if a regression model is appropriate to develop based on your sca²erplot. B. Discuss associa±ons: Based on the sca²erplot, discuss the associa±on (direc±on, strength, form) in the context of your model. b. Iden±fy any possible outliers or influen±al points and discuss their effect on the correla±on. c. Discuss keeping or removing outlier data points and what impact your decision would have on your model. C. Find r : Find the correla±on coefficient ( r ). a. Explain how the r value you calculated supports what you no±ced in your sca²erplot. Determine the Line of Best Fit. Clearly define your variables. Find and interpret the regression equa±on. Assess the strength of the model. A. Regression equa±on: Write the regression equa±on (i.e., line of best fit) and clearly define your variables. B. Interpret regression equa±on: Interpret the slope and intercept in context. C. Strength of the equa±on: Provide and interpret R -squared. a. Determine the strength of the linear regression equa±on you developed. D. Use regression equa±on to make predic±ons: Use your regression equa±on to predict how much you should list your home for based on the square footage of your home. Conclusions A. Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results. a. Did you see the results you expected, or was anything different from your expecta±ons or experiences? i. What changes could support different results, or help to solve a different problem? ii. Provide at least one ques±on that would be interes±ng for follow-up research. What to Submit To complete this project, you must submit the following: Project One Template : Use this template to structure your report, and submit the finished version as a Word document. Suppor±ng Materials The following resources may help support your work on the project: Document: Na±onal Sta±s±cs and Graphs Use this data for input in your project report. Spreadsheet: Real Estate County Data Use this data for input in your project report. Tutorial: Downloading Office 365 Programs Use this tutorial for support with Office 365 programs. Use these tutorials for support with the Excel func±ons you will use in the project: Tutorial: Random Sampling in Excel Tutorial: Sca²erplots in Excel Tutorial: Descrip±ve Sta±s±cs in Excel Tutorial: Crea±ng Histograms in Excel Project One Rubric Criteria Exemplary Proficient Needs Improvement Not Evident Value

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 4/9 inaccurate (55%) Data Analysis: Summary Sta±s±cs N/A Creates a table to show the mean, median, and standard devia±on for two variables (100%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include table showing mean, median and standard devia±on that are inaccurate or created incorrectly (55%) Does not a²empt criterion (0%) 5 Data Analysis: Interpret Graphs and Sta±s±cs N/A Interprets the graphs and sta±s±cs center, spread, shape, and any unusual characteris±c (outliers, gaps, etc.) for the two variables based on the graphs and sample sta±s±cs and compares with na±onal housing market sales (100%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include inaccurate or cursory interpreta±on of the characteris±cs of the graph and sta±s±cs or inaccurate or cursory comparison with the na±onal market (55%) Does not a²empt criterion (0%) 5 Develop Regression Model: Sca²erplot N/A Provides a graph of the sca²erplot of the data with a line of best fit; explains if a regression model is appropriate to develop based on the sca²erplot (100%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include Inaccurate sca²erplot or line of best fit or explana±on of regression model appropriateness that is inaccurate or cursory (55%) Does not a²empt criterion (0%) 5 Develop Regression Model: Discuss Associa±ons Exceeds proficiency in an excep±onally clear manner (100%) Discusses the associa±on in the context of the model based on sca²erplot, includes possible outliers or influen±al points, discusses effect on correla±on, and discusses impact of keeping or removing outliers (85%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include discussion of associa±on in the context of the sca²erplot, possible outliers, influen±al points and impact on correla±on, or Does not a²empt criterion (0%) 10

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 5/9 impacts of keeping or removing outliers that is inaccurate or cursory (55%) Develop Regression Model: Find r Exceeds proficiency in an excep±onally clear manner (100%) Finds the correla±on coefficient (r) and explains how the calculated r value supports what was no±ced in the sca²erplot (85%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include inaccurate calcula±on for r or explana±on of how the r value supports the sca²erplot that is inaccurate or cursory (55%) Does not a²empt criterion (0%) 10 Determine Line of Best Fit: Regression Equa±on N/A Writes the regression equa±on and clearly defines variables (100%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include regression equa±on that is wri²en inaccurately or variables that are not clearly defined (55%) Does not a²empt criterion (0%) 5 Determine Line of Best Fit: Interpret Regression Equa±on Exceeds proficiency in an excep±onally clear manner (100%) Interprets the slope and intercept in context (85%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include inaccurate interpreta±on of the slope and intercept (55%) Does not a²empt criterion (0%) 10 Determine Line of Best Fit: Strength of the Equa±on N/A Provides and interprets R-squared , determining the strength of the linear regression equa±on (100%) Shows progress toward proficiency, but with errors or omissions; areas for improvement may include inaccuracies in interpreta±on of R-squared or the determined strength of the regression equa±on (55%) Does not a²empt criterion (0%) 5 Determine Line of Best Fit: Use Regression Equa±on to Make N/A Uses a regression equa±on to predict how much you should list your Shows progress toward proficiency, but with errors or omissions; areas for Does not a²empt criterion (0%) 5

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 7/9

5/31/2021 Project One Guidelines and Rubric - MAT-240-X5769 Applied Statistics 21EW5 https://learn.snhu.edu/d2l/le/content/735879/viewContent/13282160/View 8/9