MAT 240 Module Two Assignment Template (1)
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Southern New Hampshire University *
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Feb 20, 2024
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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
Department of Mathematics, SNHU
MAT 240: Applied Statistics
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
The real estate industry conducted this study to assess the competitive advantage of implementing intelligent business practices. Statistical analysis techniques, including regression analysis and descriptive statistics, were employed. Regression analysis examined the correlation between property size (measured in square feet) and selling price, while descriptive statistics were utilized to summarize the data. A random sample of 30 real estate properties was selected, and their median listing price and square footage were recorded for this study.
Representative Data Sample
The provided data in Table 1 displays the results of 30 random samples collected from the West South-Central Region. Please refer to Table 2 below for a summary of the pricing of listings, cost
per square foot, and square footage.
Table 1
State
County
listing price
$'s per square foot
square feet la
tangipahoa
242,300 $110
2,202 la
st. landry
254,700 $118
2,150 tx
comal
249,200 $165
1,510 tx
dallas
279,600 $144
1,940 la
bossier
305,500 $140
2,177 la
lafourche
297,000 $128
2,320 la
st. tammany
280,300 $150
1,869 la
calcasieu
214,300 $115
1,871 la
ouachita
304,300 $158
1,921 la
terrebonne
254,700 $156
1,637 ok
tulsa
295,000 $130
2,267 tx
mclennan
407,300 $104
3,920 tx
liberty
206,400 $113
1,822 tx
hays
230,000 $154
1,490 tx
johnson
234,900 $115
2,040 tx
harris
196,200 $115
1,700 ar
baxter
286,900 $132
2,176 ar
garland
274,800 $134
2,056 tx
bell
323,100 $143
2,262
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
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tx
hidalgo
224,600 $121
1,853 tx
brazoria
246,300 $130
1,900 tx
ellis
163,400 $112
1,459 ar
saline
273,500 $114
2,396 tx
brazos
242,500 $132
1,835 tx
kaufman
264,300 $134
1,977 la
orleans
258,600 $152
1,698 ok
wagoner
209,100 $134
1,560 tx
hood
207,200 $91
2,278 tx
kerr
262,900 $139
1,888 la
st. mary
260,400 $141
1,842 Table 2
Listing Price
$ / Sq. Ft.
Sq. Ft.
Mean
258310 Mean
130.8607675 Mean
2000.533333
Median
256650 Median
132.0000075 Median
1910.5
Standard Deviation
46116.58651
Standard Deviation
17.84939983
Standard Deviation
445.8552984
The data in the table reveals that the mean listing price was $258,310, with a cost per square foot
of $130.86 and an average of 2000.53 square feet. The median listing price was $256,650, with a
cost per square foot of $132 and an average of 1910.5 square feet. The standard deviation measures the variation in the data from the mean. In this case, the standard deviation for the listing price was $46,116.59, the cost per square foot was $17.85, and the square footage was 445.86.
Data Analysis
Based on the descriptive statistics, it was evident that the average listing price from our study, which was $258,310, was lower than the national market average of approximately $342,365. It should be noted that the sample may not accurately reflect the national market as the mean value was approximately $85,000 lower than the national average. The sample selection process involved random sampling from various strata, dividing the target population into different groups, and selecting 30 samples from these groups. This sample size of 30 was considered sufficient to provide a representative representation of the population.
Scatterplot
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Selling Price and Area Analysis for D.M. Pan National Real Estate Company
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The Patterns
According to the scatter plot analysis, the square footage was the independent predictor variable, while the listing price was the response variable. Square footage was used as the predictor due to its independence and ability to forecast the selling price of real estate properties. The graphical representation indicated a positive correlation between square footage and listing price, suggesting that an increase in the area of real estate would lead to a corresponding increase
in its selling price. The shape exhibited a linear pattern, aligning positively with the trendline. A data point stood out as being significantly more prominent in square footage compared to the rest
of the data. The linear equation derived from the graph is as follows: y = 78.629x + 101,010. Based on an area of 1,800 square feet, the corresponding selling price would be calculated as follows: Listing price = 78.629 (1800) + 101,010 = $242,542.2.
The positive regression equation suggests that an increase in the independent variable would result in proportional growth of the dependent variable.