Answer:

Since our p value it's higher than the significance level provided we can conclude that at 1% of significance we FAIL to reject the null hypothesis that the slopes for the inpendent variables are equal. So we don't have a significant effect on this case.
Explanation:
Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".
The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"
When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.
If we assume that we have
independent variables and we have
individuals, we can define the following formulas of variation:
And we have this property
The degrees of freedom for the model on this case is given by
where k =2 represent the number of independent variables.
The degrees of freedom for the error on this case is given by
. Since for this case N=10 and k=2
And the total degrees of freedom would be


Now we can find the mean squares for the regression and the error, given by:


And now we can find the F statistic given by:

Now we can find the p value given by:

We can use the following excel code to verify the operation:"=1-F.DIST(4.3333,2,7,TRUE)"
Since our p value it's higher than the significance level provided we can conclude that at 1% of significance we FAIL to reject the null hypothesis that the slopes for the independent variables are equal. So we don't have a significant effect on this case.