The linear regression results below are based on commercial properties that real estate agencies use to guide clients with quantitative information helpful to make rental decisions. A random sample of 27 properties are considered with data for the variables, age of the property (X1), operating expenses and taxes (X2), vacancy rates (X3), total square footage (X4), and rental rates (Y). Parameter Estimator

The linear regression results below are based on commercial properties that real estate agencies use to guide clients with quantitative information helpful to make rental decisions. A random sample of 27 properties are considered with data for the variables, age of the property (X1), operating expenses and taxes (X2), vacancy rates (X3), total square footage (X4), and rental rates (Y). Parameter Estimator

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

The linear regression results below are based on commercial properties that real estate
agencies use to guide clients with quantitative information helpful to make rental decisions. A
random sample of 27 properties are considered with data for the variables, age of the property
(X1), operating expenses and taxes (X2), vacancy rates (X3), total square footage (X4), and
rental rates (Y).
Parameter Estimates
Coefficients Standard Error
Intercept
AGE(X1)
EXPENSE(X2)
VACRATE(X3)
SOFOOT(X4)
14.33223914
1.457614894
-0.115608327
0.031632885
0.100364547
0.14274746
0.631648649
2.043860082
0.063765767
0.024929829
What is the upper limit of the 95% confidence interval estimate for the effect of a square
footage increase on the mean rental rates of properties?

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