Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the a) OLS estimate of the slope the same b) OLS estimate of the intercept the same c) regression R2 the same d) variance of the OLS estimators the same e) none of the above remain the same

In statistics ordinary least square (OLS), an estimate of uncertain parameters in the linear regression model is a linear least-square form. OLS is the highest likelihood estimator on the basis that errors naturally are distributed.

The OLS estimator is compatible when the regressors are exogenous and efficient when the errors are homoscedastic and not strongly associated within the class of linear unbiased estimators.

Sam1s Sam1s · Answer 2 · 2021-12-05T12:06:57+01:00

a)The OLS estimate of the slope the same.

Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the OLS estimate of the slope the same.

The OLS slope variable of calculators is not based on the weight.
In measurements slightest square (OLS), an assess of questionable parameters within the linear regression show could be a straight least-square frame.
OLS is the most elevated probability estimator on the premise that mistakes actually are disseminated.

Thus, the correct answer is a.

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