MAT 240 Module Two Assignment Template (2)

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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 Timberly Swaim Southern New Hampshire University

Selling Price Analysis for D.M. Pan National Real Estate Company 2 Introduction This report will provide a detailed examination of two variables: the selling price and the square footage of a household within a specific region. This report will focus on the East North Central region. This will show the differences between the national and random sample means, medians, and standard deviation for both the listing price and the square footage. Representative Data Sample First, I selected the East North Central region, by deleting the rest of the regions along with the data that represented them. To get my random sample I used the random function in column G to produce random numbers that are smaller than zero. I then proceeded to highlight all the data presented to me for East North Central and went to the data tab in Excel and clicked the sort section where I chose random from smallest to largest to get truly random samples. The mean for the listing price is less when compared to the mean of the national listing price, This is less than expected for the mean price, this is a $128,582 difference. The median price for the sample is $212,900, while the median price for the national sample is $318,000. This is about a $105,000 difference. The standard deviation for the sample price is $38,454, while the standard deviation for the national price is $125,914, this difference is approximately $88,000. Now for the square footage, the mean square footage for the random sample is 1,647, while the national mean of square footage is 2,111. This means only a 464 difference. The median square footage for the random sample is 1,643, and the national median square footage is 1,881. This gives a difference of 238. The standard deviation of the square footage for the random samples is 282, while the standard deviation of square footage for the national is 921. Giving a difference of 639.

Selling Price Analysis for D.M. Pan National Real Estate Company 3 Data Analysis By choosing East North Central region and deleting all the data that does not correlate to East North Central, then using column G in the data on Excel that was provided to me, and labeling column G as random, Provided my random sample. Then I proceeded to use the random function all throughout column G. The function is =random(G:G). This fills column G with random numbers that are below zero. Then going to the data tab at the top of Excel and going to sort, I then proceeded to sort them by the random G column from the lowest number to the biggest. I deleted all the data that was below row 30. Giving me a truly random sample of 30. The data that was collected from the random sample clearly shows that it is significantly lower than the national in all areas of mean, median, and standard deviation for both listing price and square footage. The difference between the national and sample listing price is $128,582, how I got this, by subtracting the value of the sample from the value of the national, this will be the same for all the data collected for comparing the national and the sample due to the national being bigger than the sample, in this case. The difference for the median listing price between these two is $105,100. The difference for the standard deviation of listing price is about $88,000. Now for the square footage, the mean difference is 464. The difference for the median is 238. Finally, the difference in the standard deviation is 639. The sample should be similar in size, but in this case, it is not, the sample is less than the national size. Scatterplot 1,000 1,200 1,400 1,600 1,800 2,000 2,200 50,000

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Selling Price Analysis for D.M. Pan National Real Estate Company 4 100,000 150,000 200,000 250,000 300,000 350,000 f(x) = 92.13 x + 62046.9 Square Footage Compared to Listing Price The Pattern The y-axis is the listing price of homes within the region, the x-axis is the square footage of those homes within the region. The square footage is the predictor variable while the listing price is the response variable since the bigger the square footage, the bigger the price, same works vice versa, smaller the square footage, the less it costs. The square footage is the variable that will be the most useful for making predictions. The scatterplot has a weak slope. The scatterplot is positive. There are no major outliers in this set of data, it is quite uniform for a scatterplot. The only real pattern that can be found is that the larger the square footage, the more the listing price will be. After inputting the 1,800 into the regression equation, the listing price for a house of 1,800 sqft will cost around $64,000. To get this, replace x in the regression equation with 1,800 which should look like y=92.134(1,800) + 62047 which equals out to be $63,847.

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