Linearity in variables is necessary in the OLS modela.Falseb.True
Question
Linearity in variables is necessary in the OLS modela.Falseb.True
Solution 1
b. True
Ordinary Least Squares (OLS) is a type of linear regression analysis that is used to estimate the parameters of a linear model. The linearity in variables is necessary in the OLS model because it assumes that the relationship between the independent and dependent variables is linear. This means that the change in the dependent variable due to a one-unit change in the independent variable is constant, regardless of the value of the independent variable. If this linearity assumption is violated, the OLS estimates will be biased and inconsistent.
Solution 2
b. True
Ordinary Least Squares (OLS) is a type of linear regression analysis that is used to estimate the parameters of a linear model. The linearity in variables is necessary in the OLS model because it assumes that the relationship between the independent and dependent variables is linear. This means that the change in the dependent variable due to a one-unit change in the independent variable is constant, regardless of the current value of the independent variable.
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