Suppose a commercial developer in Vereeniging consider to purchase a group of small office buildings in an established business district. He uses multiple linear regression analysis, which was based on a sample of 35 office buildings, to estimate the value of an office building in a given area based on the following variables. Y = Assessed value of the office building (in Rand) X1= Floor space in square meters X2= Number of offices X3= Age of the office building in years Answer the questions that
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.
Suppose a commercial developer in Vereeniging consider to purchase a group of small office buildings in an established business district. He uses multiple linear
Y = Assessed value of the office building (in Rand)
X1= Floor space in square meters
X2= Number of offices
X3= Age of the office building in years
Answer the questions that follow by typing only the letter of the correct option (A, B, C, D or E) in the answer spaces provided.
Variables
y: Value
x1: Floor Space
x2: Offices
x3: Age
Model Fitting Statistics
R^2 = 0.9752
Adj R^2: ?
Regression Coefficients
Beta Parameter Standard b Parameter Standard
Estimates Error of Beta Estimates Error of b t Statistic Prob > |t|
Intcpt -17009.1 54551 -0.348 0.7505
x1 572.4 - 606.2 263 2.569 0.0826
x2 11576.3 - 10637.9 1097 9.697 0.0023
x3 -274.3 - -275.2 41 -7.157 0.0056
Calculate the value of the test statistic that is used to test whether the regression as a whole is statistically significant.
A) 40.23
B) 2145.03
C) 2.46
D) 406.33
E) 4295.22
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