To compute the 95% prediction interval for the price per share when the dividend is $10, we can follow these steps based on the provided information:

Step 1: Use the Regression Equation

The regression equation is given as:

$\text{Price} = 28.9367 + 2.4050 \times \text{Dividend}$

Substitute $\text{Dividend} = 10$ into the equation to find the predicted price:

$\text{Price} = 28.9367 + 2.4050 \times 10 = 28.9367 + 24.05 = 52.9867$

Step 2: Calculate the Standard Error for Prediction

We will calculate the prediction interval. For this, we use the formula for the prediction interval:

$\text{Prediction Interval} = \hat{y} \pm t_{\alpha/2} \cdot SE_{\text{prediction}}$

Where:

• $\hat{y}$ is the predicted value (52.9867),
• $t_{\alpha/2}$ is the critical value from the t-distribution (2.048 given),
• $SE_{\text{prediction}}$ is the standard error of the prediction.

We need to calculate the standard error $SE_{\text{prediction}}$ first. Since specific information about the standard error or residual variance isn't provided in your data, I will proceed assuming you have access to it, or it's calculable from additional data.

Do you have the standard error of the regression (or standard error of estimate) available? If not, we can only complete the interval once that information is known.

Let me know if you need further clarification or details on how to proceed!