Module 4 Assignment for Applied Statistics

Median Housing Price Model for D. M. Pan National Real Estate Company 3 My predictor variable for my sample is square feet, while my response variable is the listing price. Region State County Listing Price Square Feet West South Central TX Kerr 262,900 1,888 Mid-Atlantic NJ Ocean 547,400 4,178 East North Central IL Knox 205,100 1,740 New England MA Norfolk 394,600 1,949 East South Central TN Hawkins 269,000 2,385 Mid-Atlantic MD Baltimore 311,800 1,921 New England MA Middlesex 791,100 5,230 West South Central OK Comanche 252,100 1,806 East South Central KY Bullitt 319,300 2,527 Pacific CA Monterey 417,200 1,213 East North Central IN Wayne 203,800 1,441 West South Central LA Iberia 188,700 1,511 West North Central KS Riley 356,900 2,261 South Atlantic NC Buncombe 446,000 2,190 West South Central LA Ascension 303,200 2,030 West North Central MO St. Charles 430,300 2,302 Northeast PA Crawford 292,400 1,435 South Atlantic SC Berkeley 351,900 1,913 Pacific WA Clark 460,700 1,922 Mid-Atlantic VA Bedford 242,600 1,224 South Atlantic GA Houston 355,300 2,306 West South Central TX Liberty 206,400 1,822 Pacific CA San Bernardino 465,500 1,873 Pacific CA Santa Cruz 405,100 1,955 South Atlantic FL Hillsborough 362,900 1,994 Northeast PA Chester 786,800 5,290 East South Central AL Elmore 262,700 2,313 Pacific CA Nevada 377,400 1,614 East North Central IL Stephenson 235,600 1,682 Mountain MT Cascade 309,800 1,598 New England MA Norfolk 313,400 1,806 Mountain NM Curry 528,000 3,720 South Atlantic NC Franklin 329,700 1,871 East South Central AL Tuscaloosa 259,000 1,895 Mountain NM Eddy 599,300 3,636 Northeast NY Cayuga 523,300 3,141 East South Central MS Oktibbeha 264,400 2,135 New England MA Berkshire 422,800 2,511

Median Housing Price Model for D. M. Pan National Real Estate Company 6 but when using the = correl ( ) equation in Excel, I discovered it to be strong. The correlation coefficient for my scatterplot is 0.874, which when compared to the scatterplot confirms the assumption of a strong model. Determine the Line of Best Fit My regression equation is y = 113.98x +105550 with x being square footage and y being the listing price. The slope of this equation is 113.98, which represents the change in listing price when there is a unit change in square feet area. For example, an increase of 100 square feet will increase the price by $11,398. This is calculated by 113.98 * 100. The intercept is 105,550. This represents what the listing price would be if the was a home with 0 square footage, therefore the price of the land. To determine the strength of my equation, I need to square the value of the correlation coefficient. This is represented as r-squared = 0.874 * 0.874. R-squared ends up equaling 0.763. This means 76.3% of the variation and listing price is explained by this model, which is really good. This shows that the linear regression equation is strong. With this equation, we can predict how much a home of 1,500 square feet should be listed. Just replace x with 1,500. y = 113.98 * 1500 + 105550 = 170970 + 105550 = $276,520 Conclusions In conclusion, with the information obtained throughout my research, I’ve concluded that the listing price of a home can be determined by its square footage. My sample provided results close to the national housing market sales, which shows how reliable the data is. My results