With $F$-test in multiple linear regression, you test the $H_0$ that a "trivial" model $$y = \beta_1 + u$$ (i.e. dependent variable as a function of intercept and random error only) is equally good in predicting variability in the dependent variable as your "main" model $$y = \beta_1 + \beta_2 x_2 + \beta_3 x_3 + \beta_4 x_4 + \beta_5 x_5 + u$$.
Basically, you test the significance of the model as a whole. In $F(k, (n-k-1))$, parameter $k$ is the number of parameters restricted (4), and $n-k-1$ is the degrees of freedom in multiple linear regression - i.e. you are estimating your model with 15 observations only, which is far too low for 5 parameter-estimation.
Also, the critical value for $F(4,10)=2.6$ (at 5% sig. level), so you are fine (reject $H_0$ in favour of significant model). However, let me repeat that estimating 5 coeffs using 15 observations is generally not a good idea.