An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (ft²) yields the following computer output: (a) Is a linear model appropriate for these data? Explain. Rent 1,800 1,500- 1,200 900 600.. 1,000 1,250 1,500 1,750 2,000 Size Variable Coef Constant Size s = 102.4 -311.341 1.07707 R-squ = 94.7% Residuals (b) Interpret the slope of the regression line. (c) Interpret ² in context. 150 75 0- s.e. Coef 117.6 0.09047 -75- 900 1,200 1,500 2,000 Predicted Rent t Р -2.65 0.0294 11.9 0.0001 R-squ (adj) = 94.08

Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 29EQ
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An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (〖ft〗^2) yields following computer output: 

 

An SRS of apartment
listings in a large
northeastern city
comparing monthly
rent ($) versus size
(ft²) yields the
following computer
output:
(a) Is a linear model
appropriate for these
data? Explain.
Rent
1,800
1,500-
1,200-
900-
600..
1,000 1,250 1,500 1,750 2,000
Size
Variable
Constant
Size
s = 102.4
Coef
-311.341
1.07707
R-squ = 94.7%
1. Is a linear model appropriate for these data? Explain
2. Interpret the slope of the regression line. [^]
3. Interpret r^2 in context.
(b) Interpret the slope of the regression line.
(c) Interpret ² in context.
Residuals
150-
75-
0-
-75-
s.e. Coef
117.6
900
0.09047
t
-2.65
11.9
1,200 1,500 2,000
Predicted Rent
Р
0.0294
0.0001
R-squ (adj) = 94.08
O
Transcribed Image Text:An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (ft²) yields the following computer output: (a) Is a linear model appropriate for these data? Explain. Rent 1,800 1,500- 1,200- 900- 600.. 1,000 1,250 1,500 1,750 2,000 Size Variable Constant Size s = 102.4 Coef -311.341 1.07707 R-squ = 94.7% 1. Is a linear model appropriate for these data? Explain 2. Interpret the slope of the regression line. [^] 3. Interpret r^2 in context. (b) Interpret the slope of the regression line. (c) Interpret ² in context. Residuals 150- 75- 0- -75- s.e. Coef 117.6 900 0.09047 t -2.65 11.9 1,200 1,500 2,000 Predicted Rent Р 0.0294 0.0001 R-squ (adj) = 94.08 O
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