A sample consists of 500 houses sold in Karachi between January 2020 and December 2020. The multiple linear regression analysis is carried out to predict the house prices for investment in residential properties in Karachi, Pakistan. The output below is produced using SPSS. (300 words) Table: Coefficients Model Unstandardized Coefficients t VIF Constant 14.208 5.736 Age of house -0.299 -2.322 1.58 Square footage of the house 0.364 2.931 1.71 Income of families in the area 0.004 0.392 1.01 Transportation time to major markets -0.337 -2.619 1.90 R2 = 0.67; DW = 2.08 Dependent Variable: House price (Pakistani rupees in Million) a) You are required to write the multiple regression equation. b) How would you interpret the above ‘Output’ of a regression analysis performed in SPSS? c) From the above results, what can you say about the nature of autocorrelation? d) Is there multicollinearity in regression? How do you know?
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.
A sample consists of 500 houses sold in Karachi between January 2020 and December 2020. The multiple linear
Model Unstandardized Coefficients t
VIF
Constant 14.208 5.736
Age of house -0.299 -2.322 1.58
Square footage of the house 0.364 2.931 1.71
Income of families in the area 0.004 0.392 1.01
Transportation time to major markets -0.337 -2.619 1.90
R2 = 0.67; DW = 2.08
Dependent Variable: House price (Pakistani rupees in Million)
a) You are required to write the multiple regression equation.
b) How would you interpret the above ‘Output’ of a regression analysis performed in SPSS?
c) From the above results, what can you say about the nature of autocorrelation?
d) Is there multicollinearity in regression? How do you know?
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