Finding critical value for F statistic I am performing a hypothesis test and therefore intersted in finding the F critical value for my F statistic. Below is the anova output when I compare two models.
Analysis of Variance Table

Model 1: Y ~ X1 + X2 + X3 + X4
Model 2: Y ~ X1 + X2 + X3 + X4 + (X2 * X4) + (X3 * X4)
  Res.Df    RSS Df Sum of Sq      F Pr(>F)
1    108 127.24                           
2    106 122.05  2    5.1964 2.2566 0.1097

Could someone please help me find the F critical Value to compare against the F statistic (2.2566) ANOVA is showing? I am confused when it comes to numerator and denominator values since different sources state different rules for finding those. The data used has total 113 observations for both models.

Let's assume that $\beta_0$ is the intercept, $\beta_1$ is the regression coefficient associated to $X_1$, $\beta_2$ associated to $X_2$, $\ldots$, $\beta_5$ the coefficient associated to $X_2*X_4$ and $\beta_6$ the coefficient associated to $X_3*X_4$.
The test you are applying is for
$$H_0:\beta_{5}=\beta_6=0$$ against
$$H_1: \beta_5\neq 0 \text{ or }\beta_6\neq 0 \text{ or both}.$$
In this case, under $H_0$, the $F$ statistic has distribution $F_{2,106}$, where 2 is the degrees of freedom in the numerator due to the number of parameters you are fixing and $106 = n-p$ is the degrees of freedom in the denominator with $p=7$ and $n=113$.
Thus, for a level $\alpha= 5\%$ test, the threshold is 3.082, whereas the p-value is
$$P(F_{2,106} > 2.2566) = 0.1097.$$
