Answer :
One can use the f-statistic to test whether the overall regression equation is statistically significant, one uses the f-statistic.
A reasonably high R-squared score makes sense if your regression model includes independent variables that are statistically significant. According to the statistical significance, shifts in the dependent variable are correlated with changes in the independent variables.
The ratio of two variances is known as an F-statistic, and it bears Sir Ronald Fisher's name. Variances gauge how widely apart the data points are from the mean. When the individual data points tend to deviate further from the mean, there are more variances.
The F-test determines that it is unlikely that all of the coefficients will equal zero by adding the predictive power of all independent variables. But it's likely that none of the variables are individually important enough to be statistically significant.
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