Interpret the y – intercept of the line. - On average, when x = 0, a house has 282 square feet. On average, each increase in 1 square foot of a house increases its asking price by $282. On average, each increase in 1 square foot of a house decreases its asking price by $48, 515. On average, when x = 0, a house costs – $48, 515. We should not interpret the y – intercept in this problem. We should interpret the y – intercept, but none of the above are correct. -

Interpret the y – intercept of the line. - On average, when x = 0, a house has 282 square feet. On average, each increase in 1 square foot of a house increases its asking price by $282. On average, each increase in 1 square foot of a house decreases its asking price by $48, 515. On average, when x = 0, a house costs – $48, 515. We should not interpret the y – intercept in this problem. We should interpret the y – intercept, but none of the above are correct. -

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Equations and Inequations

Equations and inequalities describe the relationship between two mathematical expressions.

Linear Functions

A linear function can just be a constant, or it can be the constant multiplied with the variable like x or y. If the variables are of the form, x2, x1/2 or y2 it is not linear. The exponent over the variables should always be 1.

Question

The output below shows a regression between the square footage and asking price (in thosands of dollars) for
nine homes for sale in Orange County, California in February 2010.
1800
1600
1400
1200
SUMMARY OUTPUT
1000
800
Regression Statistics
Multiple R
0.908689165
600
R Square
0.825715999
400
Adjusted R Square 0.800818284
Standard Error
166.9198439
200
Observations
9
0.
1000
2000
3000
4000
5000
6000
ANOVA
df
Significance F
924032.3089 924032.3 33.16433 0.000692097
SS
MS
F
Regression
Residual
195035.64 27862.23
Total
8
1119067.949
Coefficients Standard Error
t Stat
Lower 95% Upper 95% Lower 95.0% Upper 95.0%
P-value
Intercept
-48.51516784
133.4610236 -0.36352
0.72695 -364.1003409 267.070005 -364.1003409 267.0700052
SqFt
0.281922541
0.048954677 5.758848 0.000692 0.166163124 0.39768196
0.166163124 0.397681958
What is the regression equation?
Oy =
48.52x + 0.28
%3D
y =
48.52 +0.28x
- 48.52x +0.28
ý =
-48.52+0.28x
Interpret the y
intercept of the line.
On average, when z = 0, a house has 282 square feet.
%3D
On average, each increase in 1 square foot of a house increases its asking price by $282.
On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.

Interpret the y – intercept of the line.
On average, when x = 0, a house has 282 square feet.
%3D
On average, each increase in 1 square foot of a house increases its asking price by $282.
On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.
On average, when x =
0, a house costs – $48, 515.
-
We should not interpret the y – intercept in this problem.
We should interpret the y - intercept, but none of the above are correct.
Give a practical interpretation of the coefficient of determination.
O We can predict the home asking price correctly 82.57% of the time using square footage in a least-
squares regression line.
82.57% of the sample variation in home asking price can be explained by the least-squares regression
line.
We can predict the home asking price correctly 90.87% of the time using square footage in a least-
squares regression line.
90.87% of the differences in home asking price are caused by differences in square footage.
90.87% of the sample variation in home asking price can be explained by the least-squares regression
line.
82.57% of the differences in home asking pric are caused by differences in square footage.
Is it reasonable to use the regression equation to make a prediction for a 550 square foot house? Justify your
answer.
Yes, all of the criteria are met.
No, this prediction is far outside the scope of available data.
No, r does not indicate that there is a reasonable amount of correlation.
No, the regression line does not fit the points reasonably well.

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