Given are five observations for two variables, x and y.

$$\begin{matrix} x_i & \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ \hline y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

Develop a scatter diagram for these data.

Use the following data:

$$\begin{matrix} x_i& \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

The estimated regression equation for these data is $\hat { y } = .20 + 2.60 x .$

Compute SSE, SST, and SSR using equations $( 14.8 ) , ( 14.9 ) ,$ and $( 14.10 )$ .

Given are five observations collected in a regression study on two variables.

$$\begin{matrix} x_i & \text{2} & \text{6} & \text{9} & \text{13} & \text{20}\\ \hline y_i & \text{7} & \text{18} & \text{9} & \text{26} & \text{23}\\ \end{matrix}$$

Use the estimated regression equation to predict the value of y when x = 6.

Given are five observations collected in a regression study on two variables.

$$\begin{matrix} x_i & \text{2} & \text{6} & \text{9} & \text{13} & \text{20}\\ \hline y_i & \text{7} & \text{18} & \text{9} & \text{26} & \text{23}\\ \end{matrix}$$

Develop the estimated regression equation for these data.

Given are five observations for two variables, x and y.

$$\begin{matrix} x_i & \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ \hline y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

What does the scatter diagram developed in part (a) indicate about the relationship between the two variables?

Use the following data:

$$\begin{matrix} x_i& \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

Compute the mean square error.

Given are five observations for two variables, x and y.

$$\begin{matrix} x_i & \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ \hline y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

Use the estimated regression equation to predict the value of y when x = 4.

Five observations taken for two variables follow.

$$\begin{matrix} x_i & \text{4} & \text{6} & \text{11} & \text{3} & \text{16}\\ y_i & \text{50} & \text{50} & \text{40} & \text{60} & \text{30}\\ \end{matrix}$$

Develop a scatter diagram with x on the horizontal axis.

Five observations taken for two variables follow.

$$\begin{matrix} x_i & \text{6} & \text{11} & \text{15} & \text{21} & \text{27}\\ y_i & \text{6} & \text{9} & \text{6} & \text{17} & \text{12}\\ \end{matrix}$$

Develop a scatter diagram for these data.

Data for two variables, x and y, follow.

$$\begin{matrix} x_i& \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

a. Develop the estimated regression equation for these data. b. Plot the standardized residuals versus $\hat y$. Do there appear to be any outliers in these data? Explain. c. Compute the studentized deleted residuals for these data. At the .05 level of significance, can any of these observations be classified as an outlier? Explain.

Use the following data:

$$\begin{matrix} x_i& \text{1} & \text{2} & \text{3} & \text{4} & \text{5}\\ y_i & \text{3} & \text{7} & \text{5} & \text{11} & \text{14}\\ \end{matrix}$$

Develop the 95% confidence and prediction intervals when x = 12. Explain why these two intervals are different.

Given are five observations for two variables, x and y.

a. Develop a scatter diagram for these data.

b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables?

c. Try to approximate the relationship between x and y by drawing a straight line through the data.

d. Develop the estimated regression equation by computing the values of $b_0$ and $b_1$ using equations mentioned.

e. Use the estimated regression equation to predict the value of y when x=4.

Use the following data:

$$\begin{matrix} x_i& \text{2} & \text{6} & \text{9} & \text{13} & \text{20}\\ y_i & \text{7} & \text{18} & \text{9} & \text{26} & \text{23}\\ \end{matrix}$$

The estimated regression equation for these data is $\hat { y } = 7.6 + .9 x .$ What percentage of the total sum of squares can be accounted for by the estimated regression equation? What is the value of the sample correlation coefficient?

Given are five observations for two variables, x and y.

$$\begin{matrix} x_i & \text{3} & \text{12} & \text{6} & \text{20} & \text{14}\\ \hline y_i & \text{55} & \text{40} & \text{55} & \text{10} & \text{15}\\ \end{matrix}$$

Develop the estimated regression equation by computing the values of b0 and b1 using equations.