MAT 240 SNHU Median Housing Price Prediction Model Report

I need assistance with the below project:

MAT-240 Applied Statistics

Scenario

Smart businesses in all industries use data to provide an intuitive analysis of how they can get a competitive advantage. The real estate industry heavily uses linear regression to estimate home prices, as cost of housing is currently the largest expense for most families. Additionally, in order to help new homeowners and home sellers with important decisions, real estate professionals need to go beyond showing property inventory. They need to be well versed in the relationship between price, square footage, build year, location, and so many other factors that can help predict the business environment and provide the best advice to their clients.

Prompt

You have been recently hired as a junior analyst by D.M. Pan Real Estate Company. The sales team has tasked you with preparing a report that examines the relationship between the selling price of properties and their size in square feet. You have been provided with a Real Estate County Data document that includes properties sold nationwide in recent years. The team has asked you to select a region, complete an initial analysis, and provide the report to the team.

Specifically you must address the following rubric criteria, using the Module Three Assignment Template attached.

In this module, you will also learn to further diagnose the shape, direction, and strength of a relationship between two quantitative variables in a scatterplot. You will deepen your understanding of correlation and interpret correlation coefficients in context. Moreover, you will practice determining the regression equation when provided with statistical output, and use the equation to find predicted response values and residuals. Finally, you will gain skills to interpret the intercept, slope and correlation coefficient.