Assignment 2-3

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1 Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company LaBreja Graham Department of Mathematics, SNHU MAT 240: Applied Statistics Sonal Patel 1/21/24

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 2 Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company Introduction This report is to provide an analysis of the relationship between the selling price of properties and their size of square feet from a random sample of 30 properties in a specific region. Generate a Representative Sample of the Data Region Stat e County listing price $'s per square foot square feet random East North Central in elkhart 202,300 $182 1,113 0.57156 East North Central mi midland 174,500 $151 1,157 0.99229 3 East North Central mi monroe 228,600 $136 1,679 0.85626 5 East North Central oh ashtabula 177,000 $107 1,658 0.19807 7 East North Central mi st. joseph 188,500 $135 1,397 0.64589 7 East North Central oh lake 225,900 $135 1,676 0.59239 7 East North Central in lawrence 270,600 $137 1,978 0.34475 3 East North Central oh jefferson 246,500 $136 1,814 0.27795 East North Central mi ionia 193,600 $137 1,416 0.08881 2 East North Central il ogle 236,600 $208 1,138 0.21124 4 East North Central il kankakee 148,700 $115 1,293 0.86249 9 East North Central in delaware 221,600 $134 1,651 0.17345 2 East North Central il st. clair 201,400 $164 1,225 0.62003 3 East North Central mi ingham 222,500 $125 1,777 0.95053 8 East North Central oh stark 201,000 $163 1,230 0.06990 3 East North Central oh trumbull 243,000 $133 1,827 0.62927 7

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 4 the national market. I ensured my sample is completely random by using the function on an excel spreadsheet to generate numbers by random for each listing in my selected region. From there I used the sort function in the data tab to sort each listing in the East North Central region into a random order from the random numbers generated and picked the first 30 listing out of that order. Generate Scatterplot 1,000 1,500 2,000 2,500 3,000 3,500 4,000 - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000 f(x) = 104.08 x + 53707.13 Chart Title Observe Patterns x is square feet of the listing property and y is the listing price of the property. The predictor variable x , square footage, would be more useful for making predictions. I believe there is an association between x and y, as square footage increases the price for the listing increases as well. The shape to me is liner, even though its not in a perfect line, as the square footage increased, so did the price. Based on the equation I would list it at $241,051. The equation is y=104.08x+53707, with y being the price I would want to list and x being the 1,800 square footage of the house.

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 5 Yes a potential outlier is in the scatterplot, located at point (3,518 , 461,400). I believe the outliners appeared because in the region I selected there are very few homes with a larger square footage are, and with that number being lower when generating the random sample there wasn’t many listings picked. With the listing being a larger square footage, the price for the listing is larger too making it a great distance away from other dots on the scatterplot. An outlier is considered a data value that is either much greater than or much less than the rest of the data and not representative of the rest of the data being considered.