Ga sample of 10 observations collected in a regression study on three variables, x_1(independent variable), x_2(independent variable and y(dependent variable). the sample resulted in the following data. ssr=26, sst=47 using a 0.01 level of significance, we conclude that the regression model is significant overall. (enter 1 if the conclusion is correct. enter 0 if the conclusion is wrong.)

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.

Step-by-step 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.

i pretty sure it is

more detail

step-by-step explanation: