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How many 5-digit numbers can be formed with the first 3 digits odd and the last 2 digits even?
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SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techni ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
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 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 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 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
Describe the report: Give a brief description 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 response and predictor variables in a linear regression to justify the selection of variables.
Data Collection
Sampling the data: Select a random sample of 50 houses.
Identify your response and predictor variables.
Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.
Data Analysis
Histogram: For your two variables, create histograms.
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 graph of the scatterplot of the data with a line of best fit.
Explain if a regression model is appropriate to develop based on your scatterplot.
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.
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Most Popular Content
BISM 7213 The University of Queensland Securing Business Information Questions
Got 3 question similar to what i postedsend draft in 12 hrs time, n need to draft quetsiosn
BISM 7213 The University of Queensland Securing Business Information Questions
Got 3 question similar to what i postedsend draft in 12 hrs time, n need to draft quetsiosn
Argosy University Week 7 Data Summary for The Variance Analysis Worksheet
Part one For this assignment, use data you downloaded in your W1 Project.Suppose you have information that the average str ...
Argosy University Week 7 Data Summary for The Variance Analysis Worksheet
Part one For this assignment, use data you downloaded in your W1 Project.Suppose you have information that the average stress score of students in online universities is 13.15.Using Microsoft Excel, compute a one-sample t-test to find out whether the stress scores reported by your sample are significantly different from those of the population of online students.Move your output into a Microsoft Word document.Write one paragraph to explain how you located and determined the critical value of t, and how you determined whether your obtained t-statistic was significant.Write a 1-paragraph, APA-formatted interpretation of the results modeled on the example given in your lecture.Part 2For this assignment, use data from W1 Project.This week, you will first look to see whether the type of information participants were given, whether consistent or inconsistent with what they viewed in the video, has a bearing on confidence. You will next explore the hypothesis that memory may decay over time.1. Choose and calculate the appropriate t-test to compare the confidence of participants given consistent feedback with those given inconsistent feedback.a. Move your output into a Microsoft Word document and write an interpretation of your test following the data output in one paragraph. Be sure to use APA format and write a formal report modeled on the examples given in your lecture.2. Choose and calculate the appropriate t-test to compare Recall 1 with Recall 3.a. Move your output into a Microsoft Word document and write an interpretation of your tested following the data output in one paragraph. Be sure to use APA format and write a formal report modeled on the examples given in your lecture.Do not take this question if you are not good with excel or statistics!!!!
4 pages
Deliverable05worksheet1
1. Market research has determined the following changes in the polls based on the different combinations of choices for th ...
Deliverable05worksheet1
1. Market research has determined the following changes in the polls based on the different combinations of choices for the two candidates on the tax ...
MA141 Grantham University Week 6 Precalculus: Math Quiz Sheet
Complete math quiz, I tried to copy and paste but the program did not allow me. I had to save the images on the word docum ...
MA141 Grantham University Week 6 Precalculus: Math Quiz Sheet
Complete math quiz, I tried to copy and paste but the program did not allow me. I had to save the images on the word document. I hope it does not complicate things.
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
Competencies
In this project, you will demonstrate your mastery of the following competencies:
Apply statistical techni ...
SNHU Housing Price Prediction Model for DM Pan National Real Estate Company Paper
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 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 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 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
Describe the report: Give a brief description 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 response and predictor variables in a linear regression to justify the selection of variables.
Data Collection
Sampling the data: Select a random sample of 50 houses.
Identify your response and predictor variables.
Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.
Data Analysis
Histogram: For your two variables, create histograms.
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 graph of the scatterplot of the data with a line of best fit.
Explain if a regression model is appropriate to develop based on your scatterplot.
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.
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