mat-240 module 2 project

I was recently hired as a junior analyst at D.M Pan National Real Estate Company and was asked to prepare a report to examine the relationship between the selling prices of properties and their size in square feet. I was given the data that I was asked to review and select a random region to create a random sample like in this sample. Sample # State Listing Price Square Feet Sample 1 wa $430,400 1,680 Sample 2 wa $887,800 4,422 Sample 3 Ca $354,600 1,390 Sample 4 Ca $516,300 1,914 Sample 5 Or $385,500 1,728 Sample 6 Ak $330,600 1,430 Sample 7 Ca $349,500 1,494 Sample 8 Or $676,100 3,656 Sample 9 Wa $309,400 1,296 Sample 10 Ca $371,200 1,597 Sample 11 Wa $420,400 1,963 Sample 12 Hi $512,300 2,030 Sample 13 Ca $424,000 1,959 Sample 14 Or $351,000 1,192 Sample 15 Ca $291,500 1,655

Sample 16 Wa $422,800 1,462 Sample 17 Ca $777,900 4,635 Sample 18 Wa $370,100 1,351 Sample 19 Or $387,200 1,526 Sample 20 Or $357,000 1,324 Sample 21 Ca $413,100 1,595 Sample 22 Ca $654,300 3,597 Sample 23 Wa $405,700 1,800 Sample 24 Wa $500,200 1,993 Sample 25 Wa $460,700 1,922 Sample 26 Or $345,500 1,104 Sample 27 Ca $329,500 1,274 Sample 28 Ca $364,800 1,546 Sample 29 Wa $420,400 1,856 Sample 30 ca $417,200 1,213 Using the above information, I was able to find the mean, median and the standard deviation for my sample as shown below. Mean Median Standard Deviation Listing Price 441,233 409,400 $138,813 Square Feet 1,920 1,626 914.631 The random sample I have provided as you can see is not a great representation for the national market. If you look at the National Summary Statistics and graphs real estate data, we

random sample for this study. This was truly randomized computer-generated numbers.

Selling Price Analysis for D.M. Pan National Real Estate Company 4 largest. Once by the random values applied to each entry, I choose the top 30 results to create the random sample used within my study. The results resulted in a true random selection of 30 entries that show no biases that could be present in selecting a

The correlation of x and y on the scatterplot above shows that the median square footage rises so does the median listing price of the home. This means that the plot indicates that there is an evident positive relationship between the two values and that the relationship is linear which you can see on the scatterplot. The regression formula of scatterplot is Y=144.28x + 164,188 and if the house wsa 1800 square feet based on this equation the price you would list the house for would be equivalent to Y=144.28*1,800+164.188 which equals Y=144.28*1,800+164.188 and Y=259,800 + 164.188. This means that Y=$259,800+ 164.188 which means that Y=$259,868 as the listing price for a house with 1,800 square feet in the Pacific region. The biggest outliers that are on the scatterplot are two homes listed at $887,800 which has 4,422 square feet and $779,900 with 4,635 square feet. Based on the regression equation provided by the scatterplot the first house should have been listed at &638,170 and the second house should have been listed at $668,902. The first house was listed a lot cheaper compared to the regression equation by about $249,630 and the second house was listed at a higher rate compared to the regression equation by about $108,988. Some reasons there could be a difference is because of where the house is located (city), amenities, or how new the house is. The outliers are evident on the scatterplot to show houses that fall outside of the normal median variables provided on the scatterplot.

144.28x + 164,188 and if you had a house with 1,800 square feet based on this equation the price you would list the house at would be equivalent to. Y = 144.28*1,800 + 164.188 which equals Y = 144.28*1,800 + 164.188 and Y = 259,800 + 164.188. Which means that Y = $259,868.19 or

$259,868 dollars as the listing price for a house with 1,800 square feet in the Pacific region. There are a few outliers on the scatterplot the largest two being a home listed at $887,800 with 4,422 square feet and $777,900 with 4,635 square feet. Based on the regression equation provided by the scatterplot the first

equation by about $108,988. The reasons for these differences could be what city the houses are located in. How old or new the houses are. What amenities the houses offer or don’t offer for that matter. The outliers are evident on the scatterplot to show houses that fall outside of the normal

median variables provided on the scatterplot. The above 2 examples show and explain why outliers may exist within the scatterp Selling Price Analysis for D.M. Pan National Real Estate Company 2 Introduction I was hired as a junior analyst by D.M Pan National Real Estate Company and have bban

Representative Data Sample I choose to review the Pacific region as I am originally from the West Coast in the USA. Here is the sample that I used: Sample # State Listing Price Square Feet Sample 1 wa $430,400 1,680 Sample 2 wa $887,800 4,422 Sample 3 ca $354,600 1,390

Sample 4 ca $516,300 1,914 Sample 5 or $385,500 1,728 Sample 6 ak $330,600 1,430 Sample 7 ca $349,500 1,494 Sample 8 or $676,100 3,656 Sample 9 wa $309,400 1,296 Sample 10 ca $371,200 1,597 Sample 11 wa $420,400 1,963

Sample 20 or $357,000 1,324 Sample 21 ca $413,100 1,595 Sample 22 ca $654,300 3,597 Sample 23 wa $405,700 1,800 Sample 24 wa $500,200 1,993 Sample 25 wa $460,700 1,922 Sample 26 or $345,500 1,104 Sample 27 ca $329,500 1,274

Sample 28 ca $364,800 1