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This analysis focuses on the evaluation of t-statistics and F-statistics within unrestricted and restricted regression models. It discusses methods for excluding independent variables with low t-statistics and the implications of using critical t-values to determine variable significance. By setting a threshold defined by the critical t-value, researchers can implement F-statistics to test hypotheses about the inclusion of variables. The results direct whether to reject or not reject the null hypothesis (H0), based on comparative values between F-statistic and F-critical. Furthermore, the discourse involves testing for multicollinearity and heteroskedasticity.
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Unrestricted Model Data Theory t-statistics Restricted Model (excludes independent variables with low t-stats) F-statistic
Unrestricted Model Data Theory Examine t-statistics Restricted Model (excludes independent variables with t-stats below critical-t) lower the critical-t F-statistic F>Fcritical => reject H0 F<Fcritical => do not reject H0 Finished Model
Unrestricted Model Data Theory • Are t-statistics • correct? Test for • multicollinearity • heteroskedasticity Restricted Model (excludes independent variables with t-stats below critical-t) lower the critical-t F-statistic with H0: excluded variables do not belong in model F>Fcritical => reject H0 F<Fcritical => do not reject H0 Finished Model
