4-3 Project One Submission

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Southern New Hampshire University *

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Economics

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Apr 3, 2024

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docx

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Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company Southern New Hampshire University Juanita Onasanya Southern New Hampshire University MAT240: Applied Statistics Ole Forsberg March 27, 2024
Introduction This analysis determines if the square footage of a property is a reliable indication of the market price of that home. It employs a random sample of fifty homes from various counties in the United States, including the square footage of each property and the listed price of each house. We use linear regression to describe the connection between the variables, and the scatter plot illustrates a linear connection connecting each of the variables. The listing price would be considered the dependent variable (y), whereas the square footage would be considered the independent variable (x). The analysis's findings indicate a positive correlation between the square footage of a property and its listing price; however, this correlation is neither strong nor weak. In addition, the presence of outliers in the data may have an impact on the reliability of the model used for forecasting. Data Collection When producing an objective sample that can be used to accurately represent a population, the sampling process used is an essential component. For the purpose of doing this, I used a sampling technique that included assigning a number between 0 and 1 to each county by means of the =RAND function on the computer. Following that, I arranged the numbers in ascending order, starting with the least and working my way up to the biggest. After much deliberation, I chose the initial fifty counties to be utilized as the sample.
For the purpose of this analysis, the response variable is denoted by the letter (y), which stands for the listed price of properties. In the next step, the square footage of dwellings serves as the predictor variable, and it is denoted by the letter (x). Data Analysis
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