An economist estimates the following regression model:y = β0 + β1x1 + β2x2 + εThe estimates of the parameters b1 and b2 are not very large compared with their respective standard errors. But the size of the coefficient of determination indicates quite a strong relationship between the dependent variable and the pair of independent variables.Having obtained these results, the economist strongly suspects the presence of multicollinearity. Since his chief interest is in the influence of X1 on the dependent variable, he decides that he will avoid the problem of multicollinearity by regressing Y on X1 alone.Comment on this strategy.
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.
An economist estimates the following regression model:
y = β0 + β1x1 + β2x2 + ε
The estimates of the parameters b1 and b2 are not very large compared with their respective standard errors. But the size of the coefficient of determination indicates quite a strong relationship between the dependent variable and the pair of independent variables.
Having obtained these results, the economist strongly suspects the presence of multicollinearity. Since his chief interest is in the influence of X1 on the dependent variable, he decides that he will avoid the problem of multicollinearity by regressing Y on X1 alone.
Comment on this strategy.
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