Data were collected from a random sample of 270 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDRdenote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denotethe lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the conditionof the house is reported as "poor."%3DAn estimated regression yieldsPrice = 108.5+0.441BDR+ 21.3Bath + 0.142Hsize + 0.002Lsize+ 0.082Age - 44.4Poor, R =066, SER = 37.8.Suppose that a homeowner converts part of an existing family room in her house into a new bathroom. What is the expected increase in thevalue of the house?The expected increase in the value of the house is $(Round your response to the nearest dollar.)Suppose that a homeowner adds a new bathroom to her house, which increases the size of the house by 91 square feet. What is theexpected increase in the value of the house?The expected increase in the value of the house is $(Round your response to the nearest dollar.)What is the loss in value if a homeowner lets his house run down so that its condition becomes "poor"?The loss is S(Round your response to the nearest dollar )Compute the R for the regression.The R for the regression is(Round your response to three decimal places.)Enter your answer in each of the answer boxes.(DELL)0日P Type here to search

Given, an estimated regression yield as-…

Data were collected from a random sample of 270 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor." An estimated regression yields Price = 108.5 + 0.441BDR+21.3Bath + 0.142Hsize + 0.002Lsize + 0.082Age - 44.4Poor, R =0.66, SER= 37.8.

Data were collected from a random sample of 270 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor." An estimated regression yields Price = 108.5 + 0.441BDR+21.3Bath + 0.142Hsize + 0.002Lsize + 0.082Age - 44.4Poor, R =0.66, SER= 37.8.

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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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

100%

Data were collected from a random sample of 270 home sales from a community in 2003. Let Price denote the selling price (in $1,000), BDR
denote the number of bedrooms, Bath denote the number of bathrooms, Hsize denote the size of the house (in square feet), Lsize denote
the lot size (in square feet), Age denote the age of the house (in years), and Poor denote a binary variable that is equal to 1 if the condition
of the house is reported as "poor."
%3D
An estimated regression yields
Price = 108.5+0.441BDR+ 21.3Bath + 0.142Hsize + 0.002Lsize
+ 0.082Age - 44.4Poor, R =066, SER = 37.8.
Suppose that a homeowner converts part of an existing family room in her house into a new bathroom. What is the expected increase in the
value of the house?
The expected increase in the value of the house is $
(Round your response to the nearest dollar.)
Suppose that a homeowner adds a new bathroom to her house, which increases the size of the house by 91 square feet. What is the
expected increase in the value of the house?
The expected increase in the value of the house is $
(Round your response to the nearest dollar.)
What is the loss in value if a homeowner lets his house run down so that its condition becomes "poor"?
The loss is S
(Round your response to the nearest dollar )
Compute the R for the regression.
The R for the regression is
(Round your response to three decimal places.)
Enter your answer in each of the answer boxes.
(DELL)
0日
P Type here to search

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