Median Housing Price Prediction Model for D. M. Pan National Real Estate Company

Median Housing Price Model for D. M. Pan National Real Estate Company 3

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Report: Median Housing Price Prediction Model for D. M. Pan National Real Estate Company

[Your Name]

Median Housing Price Prediction Model for D. M. Pan National Real Estate Company 1

Southern New Hampshire University

Introduction

[Describe the report: Include in this section a brief overview, including the purpose of the report and your approach.]

Data Collection

[Sampling the data: Outline how you obtained your sample data, including the response and predictor variables.]

[Scatterplot: Insert a correctly labeled scatterplot of your chosen variables.]

Data Analysis

[Describe your study briefly. Discuss the requirements of the data sets for a linear regression. Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.]

[Histogram: Insert the histogram of the two variables. Be sure to include appropriate labels.]

[Summary statistics: Insert a table to show the summary statistics.]

[Interpret the graphs and statistics: Describe the shape, center, spread, and any unusual characteristic (outliers, gaps, etc.) and what they mean based on your sample data and the graphs you created.]

[Explain how these characteristics of the sample data compare to the same characteristics of the national population. Also, determine whether your sample is representative of the national housing market sales.]

The Regression Model

[Scatterplot: Include the scatterplot graph of the sample with a line of best fit.]

[Based on your graph, explain whether a regression model can be developed for the data and how.]

[Discuss associations: Explain the associations in the scatterplot, including the direction, strength, form in the context of your model.]

[Find rCalculate the correlation coefficient and explain how it aligns with your interpretation of the data from the scatterplot.]

The Line of Best Fit

[Regression equation: Insert the regression equation.]

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

[Strength of the equation: Interpret the strength of the regression equation, R-squared.]

[Use regression equation to make predictions: Use the regression equation to make a sample prediction.]

Conclusions

[Summarize findings: Summarize your findings in clear and concise plain language. Outline any questions arising from the study that might be interesting for follow-up research.]

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