Module 2 Assignment for Applied Statistics

docx

School

Southern New Hampshire University *

*We aren’t endorsed by this school

Course

240

Subject

Finance

Date

Jan 9, 2024

Type

docx

Pages

Uploaded by CommodoreResolveHedgehog35

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 Caitlynne Moreland Southern New Hampshire University

Selling Price Analysis for D.M. Pan National Real Estate Company 2 Introduction This report examines the relationship between the selling price of properties and their size in square feet. This report aims to analyze the data of a selected region compared to data on a national level. Representative Data Sample The region I chose was East South Central. I chose my sample randomly by using the Excel random function by inputting = random( ) and double-clicking the lower right corner of the cell. Hence, it copied the formula for each cell down to the last one for the East South Central region. I then selected every column, clicked data, clicked sort, then sorted it by random. This sorted all the East South Central region data randomly, so it was unbiased when selecting the first 30 for my sample. Sample National Mean Price $301,663 $342,365 Median Price $276,450 $318,000 Standard Deviation Price $108,941 $125,914 Sample National Mean Square Feet 2,561 2,111 Median Square Feet 2,312 1,881 Standard Deviation Square Feet 972 921 Data Analysis With the random sample I selected compared to the national market, it is evident that the price is significantly lower than the national market while having higher square footage. With this data, you can see that in the East South Central region, you get more square footage for the price than on a national average.

Selling Price Analysis for D.M. Pan National Real Estate Company 3 Scatterplot y = 104.85x + 33,171 The Pattern The x variable represents square feet for the South East Central region houses. Square feet is my x variable because it’s the independent variable of my graph. The y variable represents the listing price for the homes in my chosen region. The listing price is my y variable because it depends on the house's square footage. The variable useful in making predictions is the x variable, or square footage, because the price depends on how much square footage a house has. Not the other way around. By looking at the graph, there is a clear association between the x and y variables. As square footage increases, the price of the house increases. If square footage decreases, so does the listing price. This creates a linear-shaped graph. 0 1000 2000 3000 4000 5000 6000 7000 $0 $100,000 $200,000 $300,000 $400,000 $500,000 $600,000 $700,000 f(x) = 104.85 x + 33171.48 East South Central Region Square Feet Listing Price

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help

Selling Price Analysis for D.M. Pan National Real Estate Company 4 There are four potential outliers I can identify on this graph. The reason I believe the outliers appeared was due to my selection being random. With it being random, a few samples ended up being on the higher end of square footage and listing price. The majority of my samples are contained between 1,000 to 3,000 square feet and $100,000 to $400,000 listing price. Very few houses end up with over 4,000 square feet, with a listing price over $500,000. An example I can use to predict the listing price of a house would be if I had a 1,800-square-foot house. It can be plugged in like this: y = 104.85(1,800) + 33,171 y = 188,730 + 33,171 y = $221,901 So, by inputting 1,800 into the regression equation, it is safe to say we can list our 1,800-square- foot house at $221,901. We can also compare this data with those close to it on the graph.