MAT 240 Week 4 Assignment | Southern New Hampshire University
- southern-new-hampshire-university / MAT 240
- 15 Jun 2021
- Price: $25
- Mathematics Assignment Help / statistics
MAT 240 Week 4 Assignment | Southern New Hampshire University
Project One Guidelines and Rubric
Competencies
In this project, you will demonstrate your mastery of the
following competencies:
- Apply
statistical techniques to address research problems
- Perform
regression analysis to address an authentic problem
Overview
The purpose of this project is to have you complete all of the
steps of a real-world linear regression research project starting with
developing a research question, then completing a comprehensive statistical
analysis, and ending with summarizing your research conclusions.
Scenario
You have been hired by the D. M. Pan National 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 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 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 national descriptive
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 County Data 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
A. Describe the report: Give
a brief description of the purpose of your report.
a. Define
the question 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
scatterplot to look like?
c. Explain
the difference between response and predictor variables in a linear regression
to justify the selection of variables.
Data Collection
A. Sampling the data: Select
a random sample of 50 counties.
a. Identify
your response and predictor variables.
B. Scatterplot: Create
a scatterplot of your response and predictor variables to ensure they are
appropriate for developing a linear model.
Data Analysis
A. Histogram: For
your two variables, create histograms.
B. Summary statistics: For
your two variables, create a table to show the mean, median, and standard
deviation.
C. Interpret the graphs and statistics:
a. Based
on your graphs and sample statistics, interpret the center, spread, shape, and
any unusual characteristic (outliers, gaps, etc.) for the two variables.
b. 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
A. Scatterplot: Provide
a graph of the scatterplot of the data with a line of best fit.
a. Explain
if a regression model is appropriate to develop based on your scatterplot.
B. Discuss associations: Based
on the scatterplot, discuss the association (direction, strength, form) in the
context of your model.
b. Identify
any possible outliers or influential points and discuss their effect on the
correlation.
c. Discuss
keeping or removing outlier data points and what impact your decision would
have on your model.
C. Find r: Find the
correlation coefficient (r).
b.
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.
A. Regression equation: Write
the regression equation (i.e., line of best fit) and clearly define your
variables.
B. Interpret regression equation: Interpret
the slope and intercept in context.
C. Strength of the equation: Provide
and interpret R-squared.
a. Determine
the strength of the linear regression equation you developed.
D. 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
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
expectations or experiences?
i.
What changes could support different results, or help to solve a
different problem?
ii.
Provide at least one question that would be interesting for
follow-up research.