A local real estate agent argues that house prices in Pittsburgh are related to the number of rooms in a house. You collect a random sample of 190 houses and run a simple regression using House Price (measured in dollars) as the dependent variable and Rooms (measured as the number of rooms in a house) as the independent variable. Given below is the Excel output of the regression results. (a) Construct the 99.7% confidence interval for the slope of the regression line. (b) Consider a house with 5 room. What is the probability that the house will sell for more than $270,245? (c) A savvy real estate investor, Noah, owns a house in Pittsburgh that he is planning to sell. Based on the regression put, Noah decides to destroy some walls to reduce the number of rooms in the house, hoping to increase the house selling price. Based on the regression output, do you think this is a good decision? Justify your comment.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 17HP
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A local real estate agent argues that house prices in Pittsburgh are related to
the number of rooms in a house. You collect a random sample of 190 houses and run a
simple regression using House Price (measured in dollars) as the dependent variable
and Rooms (measured as the number of rooms in a house) as the independent
variable. Given below is the Excel output of the regression results.

(a) Construct the 99.7% confidence interval for the slope of the regression
line.
(b) Consider a house with 5 room. What is the probability that the house
will sell for more than $270,245?
(c) A savvy real estate investor, Noah, owns a house in Pittsburgh that
he is planning to sell. Based on the regression put, Noah decides to destroy some
walls to reduce the number of rooms in the house, hoping to increase the house
selling price. Based on the regression output, do you think this is a good decision?
Justify your comment.

 

Dependent Variable:
Independent Variable:
Regression Statistics
Summary Table
Variable
Intercept
Rooms
Fcst#1
Fcst#2
Fcst#3
House Price
Rooms
R Square
0.003
Forecasted: House Price
Coeff.
245870
-539
Rooms
1
5
10
Adj.RSqr
0.000
Std.Err.
5810
760
Forecast
245331
243175
240480
Std.Err.
26950
t Stat.
42.318
-0.709
StErrFst
27430
27070
27210
# Cases
190
P-value
0.000
0.478
Transcribed Image Text:Dependent Variable: Independent Variable: Regression Statistics Summary Table Variable Intercept Rooms Fcst#1 Fcst#2 Fcst#3 House Price Rooms R Square 0.003 Forecasted: House Price Coeff. 245870 -539 Rooms 1 5 10 Adj.RSqr 0.000 Std.Err. 5810 760 Forecast 245331 243175 240480 Std.Err. 26950 t Stat. 42.318 -0.709 StErrFst 27430 27070 27210 # Cases 190 P-value 0.000 0.478
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