I Is the F-test Calculation Different for Longitudinal Data in Linear Regression?

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Using a standard F-test for longitudinal data in linear regression typically requires modifications due to the nature of the data. Longitudinal data involves repeated measurements over time, which can violate the assumptions of traditional linear regression. In the context of random effects and fixed effects models, the calculation of the F-test may differ from that of cross-sectional data. It is essential to account for the correlation of observations within subjects when interpreting the F-test results. Therefore, specific adjustments or alternative methods may be necessary for accurate analysis in longitudinal studies.
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Can i Use a standard F-test on longitudinal data for a linear multiple regression?

Mons
 
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What are you trying to do, exactly? Generally you wouldn't use linear regression for longitudinal data without some modifications.
 
I am studying companies over 7 years with 4 independent variables. I am using both random effects model and fixed effects model. The regression results include an F-test for the coefficients of the independent variables. But when I am to describe how the F-test is calculated, is it different from a situation where all data is collected from a single "moment"?

Mons
 
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I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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