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Price =	28.9367	+	2.4050	dividend
Reject H0 if t is not between ±	2.048	.
The test statistic is	7.3271	.
R2	0.6572
Correlation coefficient	0.8107
What is the 95% prediction interval of price per share if the dividend is $10? (Round your answers to 4 decimal places.)
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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!

What is the 95% prediction interval of price per share if the dividend is $10? (Round your answers to 4 decimal places.)

Learn how to calculate the 95% prediction interval for the price per share using the regression equation Price = 28.9367 + 2.4050 * Dividend, with a given dividend of $10. This detailed guide covers prediction intervals, regression analysis, and statistical methods, suitable for undergraduate-level students.

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