a. compute the three sums of squares, $SST,SSR,SSE$, using the defining formulas

b. verify the regression identity,$SST=SSR+SSE$

c. compute the coefficient of determination.

d. determine the percentage of variation in the observed values of the response variable that is required by the regression

e. State how useful the regression equation appears to be for making predictions.

$\hat{y}=1.75+0.25x$

The given data is

$\hat{y}=1.75+0.25x$

The regression equation is

$\hat{y}=1.75+0.25x$

The formulas to calculate the sum of squares is

$$\begin{gathered} SST=\sum {\left(y_i-\overline{y}\right)}^2 \\ SSR=\sum {\left({\hat{y}}_i-\overline{y}\right)}^2 \\ SSE=\sum {\left(y_i-\hat{y}\right)}^2 \end{gathered}$$

As shown in the table below, the relevant sums can be determined.

$$\begin{gathered} SST=5 \\ SSR=4 \\ SSE=1 \end{gathered}$$

The given data is

$\hat{y}=1.75+0.25x$

From the above values

$SSR+SSE=1+4$

$$\begin{gathered} =5 \\ =SST \end{gathered}$$

The given data is

$\hat{y}=1.75+0.25x$

The coefficient of determination is

$r^2=\frac{SSR}{SST}$

$$\begin{gathered} =\frac{1}{5} \\ =0.2 \end{gathered}$$

The given data is

$\hat{y}=1.75+0.25x$

The coefficient of determination restated as a percentage is the percentage of variation:

$0.2=20\%$

The given data is

$\hat{y}=1.75+0.25x$

The regression equation can be used to generate predictions if the estimated $r^2$is near to $1$.

The calculated $r^2$is $0.2$, which is not equal to one.

As a result, utilising the regression equation to generate predictions is useless, because the regression can only explain $20\%$of the volatility.