What does the model predict will be the sale price of a comparable apartment in the same location with a floor area of 750 m2? How confident are you in this estimate? Explain.

College Algebra
1st Edition
ISBN:9781938168383
Author:Jay Abramson
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Chapter9: Sequences, Probability And Counting Theory
Section9.7: Probability
Problem 2SE: What is a sample space?
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ii)
What does the model predict will be the sale price of a comparable apartment in the same
location with a floor area of 750 m2? How confident are you in this estimate? Explain.
Transcribed Image Text:ii) What does the model predict will be the sale price of a comparable apartment in the same location with a floor area of 750 m2? How confident are you in this estimate? Explain.
A councellor in a new residential area of outer Western Sydney, is reviewing apartment floor
areas and sale prices to determine if he could estimate the sale price from the apartment floor
area. The following data was collected for a sample of 10 recent sales of apartments.
a)
Area (m2)
460
500
540
540
580
610
620
680
730
790
Price ($000s)
230
236
242
246
200
250
256
262
250
274
You may use the following Excel print-out to answer the questions.
Regression Statistics
Descriptive Statistics
Multiple R
0.630
n =
10
R Square
0.397
605
Adjusted R Square
0.322
y =
244.6
Standard Error
16.566
s =
10761.111
Observations
10
s3 =
404.489
Sxy =
1314.444
Coeff
Stand Error
t Stat
P-value
Intercept
170.701
32.628
5.232
0.001
Area (m²)
0.122
0.053
2.295
0.051
i)
What is the equation of the regression line to predict Sale Price from floor area?
Transcribed Image Text:A councellor in a new residential area of outer Western Sydney, is reviewing apartment floor areas and sale prices to determine if he could estimate the sale price from the apartment floor area. The following data was collected for a sample of 10 recent sales of apartments. a) Area (m2) 460 500 540 540 580 610 620 680 730 790 Price ($000s) 230 236 242 246 200 250 256 262 250 274 You may use the following Excel print-out to answer the questions. Regression Statistics Descriptive Statistics Multiple R 0.630 n = 10 R Square 0.397 605 Adjusted R Square 0.322 y = 244.6 Standard Error 16.566 s = 10761.111 Observations 10 s3 = 404.489 Sxy = 1314.444 Coeff Stand Error t Stat P-value Intercept 170.701 32.628 5.232 0.001 Area (m²) 0.122 0.053 2.295 0.051 i) What is the equation of the regression line to predict Sale Price from floor area?
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