Hinchlciff_Megan_MAT_240_Module_Three_Assignment

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Housing Price Prediction Model for D.M. Pan Real Estate Company Megan Hinchcliff Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2 Module Two Notes listing price and square feet of the Pacific Region and the National Statistics comparison Pacific Region Listing Price Square Feet National Statistics Listing Price Square Feet Mean $403,253 1,746 Mean $342,365 2,111 Median $388,850 1,629 Median $318,000 1,881 Standard Deviation $110,695 771.51 Standard Deviation $125,914 921 Regression Equation Regression Equation y = 137.54x + 163123 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 f(x) = 137.54 x + 163123.3 R² = 0.92 Pacific Region Square Feet Listing Price Determine r The relationship between the predictor value (X) and the response value (Y) is defined by determining the r correlation. When the r correlation value is between -1 and 0, it shows a negative correlation which means that as one variable increases, the other variable decreases.

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 3 When the r value is between 0 and +1, it shows a positive correlation which means that as one variable increases, the other variable also increases. In the analysis of sampling 30 homes in the Pacific Region, the correlation between the listing price and the square footage was r = 0.9586. With this information, we can determine the strength of the relationship as a strong correlation because the value is closest to 1. The direction of the correlation is also positive because as the square footage increases, the listing price also increases, as seen in the scatterplot, with the trend line rising to the right. Examine the Slope and Intercepts The slope of a regression line indicates the rate of change in listing price per unit change in the square footage. The Y-intercept represents the point where the regression line crosses the Y-axis when the X value is 0. These two values show the relationship between the listing price and the square footage of homes. The slope of the regression line in the above scatterplot is 137.54, and the intercept is 163,123. Looking at the slope, we can conclude that when the square footage increases by one, the listing price will increase by $137.54. When looking at the intercept, we can also conclude that when the square footage of a home is 0, the listing price of the land is $163,123. The intercept makes sense based on the line of best fit because it shows the listing price of land when a house is not present. R -squared Coefficient The R-squared Coefficient, also known as the coefficient of determination, determines the amount of variation in the listing price that the square footage can explain. Which shows how well the data fits in the regression line. The Coefficient of determination for the Pacific Region

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Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 4 sample is R² = 0.9198. By moving the decimal point over two units, we can determine the percentage of variation, which is 91.98%. This means that the variation in the square footage explains 91.98% of the variation in the listing price. Conclusions As seen in the table in Module Two Notes, the mean, median, and standard deviation of the listing price of homes in the Pacific Region sample are greater than the National statistics. On the other hand, the mean, median, and standard deviation of the square footage in the Pacific Region are less than the National Statistics. This indicates that you can expect to pay higher prices for homes with lower square footage. For every 100 square feet of homes in the Pacific Region, the listing price will increase by $13,754. This graph would be best used for square footage ranging between 1,000 to 6,000 to represent the minimum and maximum square feet of the homes in the sample.

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