15. For Suppose a commercial developer is consideringpurchasing aestablished business district. The developer can use multiplelinear regression analysis to estimate the value of an officebuilding in each area based on the following variables.group of small office buildings in any = Assessed value of the office buildingx1 = Floor space in square feetx2 = Number of officesx3= Number of entrancesx4 = Age of the office building in yearsISUMMARY OUTPUTRegression Statistics0.99837267Multiple RR SquareAdjusted R Square 0.99457999Standard Error0.99674799970.578463Observations11ANOVADfSSMSF1732393319|4.33E+08RegressionResidual459.75367426 5652135.316| 942022.6Total10 1738045455Standardt StatCoefficients52317.8305ErrorP-value12237.3616 4.275254|0.005232793764Floor Space (x1) 27.6413874 5.429374042 5.091082|0.00224096238112529.7682 400.0668382 31.31919|0.000000070386InterceptOffices (x2)2553.21066 530.6691519 4.811304|0.002966281803|-234.23716 13.26801148 -17.6543 0.000002120610Entrances (x3)Age (x4)(a). Write down the multiple regression equation. ·(b). How much is the % variation in the value of an officebuilding that is explained by the independent variables.(c). Interpret the regression coefficients X3.(d). At the 5% level of significance, which of the 4 independentvariables is NOT statistically significant in the determinationof the value of an office building? Explain your answer.

Given that y = Assessed value of the office building x1 = Floor space in square feet x2 = Number of…

Answered: Write down the multiple regression…

Write down the multiple regression equation. - How much is the % variation in the value of an office building that is explained by the independent variables. Interpret the regression coefficients X3.

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

15. For Suppose a commercial developer is considering
purchasing a
established business district. The developer can use multiple
linear regression analysis to estimate the value of an office
building in each area based on the following variables.
group of small office buildings in an
y = Assessed value of the office building
x1 = Floor space in square feet
x2 = Number of offices
x3= Number of entrances
x4 = Age of the office building in years
ISUMMARY OUTPUT
Regression Statistics
0.99837267
Multiple R
R Square
Adjusted R Square 0.99457999
Standard Error
0.99674799
970.578463
Observations
11
ANOVA
Df
SS
MS
F
1732393319|4.33E+08
Regression
Residual
459.7536742
6 5652135.316| 942022.6
Total
10 1738045455
Standard
t Stat
Coefficients
52317.8305
Error
P-value
12237.3616 4.275254|0.005232793764
Floor Space (x1) 27.6413874 5.429374042 5.091082|0.002240962381
12529.7682 400.0668382 31.31919|0.000000070386
Intercept
Offices (x2)
2553.21066 530.6691519 4.811304|0.002966281803
|-234.23716 13.26801148 -17.6543 0.000002120610
Entrances (x3)
Age (x4)
(a). Write down the multiple regression equation. ·
(b). How much is the % variation in the value of an office
building that is explained by the independent variables.
(c). Interpret the regression coefficients X3.
(d). At the 5% level of significance, which of the 4 independent
variables is NOT statistically significant in the determination
of the value of an office building? Explain your answer.

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