To calculate the regression equation that predicts the price per share based on the annual dividend, we need to use the linear regression formula:

$y = a + bx$

Where:

• $y$ is the dependent variable (price per share),
• $x$ is the independent variable (dividend),
• $a$ is the y-intercept (constant),
• $b$ is the slope (coefficient of the dividend).

The slope $b$ and intercept $a$ are calculated using these formulas:

$b = \frac{n\sum xy - \sum x \sum y}{n\sum x^2 - (\sum x)^2}$

$a = \frac{\sum y - b\sum x}{n}$

Where:

• $n$ is the number of data points (in this case, 30),
• $\sum x$ is the sum of dividends,
• $\sum y$ is the sum of prices,
• $\sum xy$ is the sum of the product of price and dividend for each company,
• $\sum x^2$ is the sum of the squares of the dividends.

Since you've provided only a partial dataset, I’ll assume you have access to the full data (for all 30 companies). Using this data, the values for $\sum x$, $\sum y$, $\sum xy$, and $\sum x^2$ can be computed, and the regression equation can be determined accordingly.

You can either provide the complete dataset, or I can help you with the next steps if the full information is given.

Would you like details on how to input the values into the formulas or have any questions? Here are some relative questions that expand on this topic:

1. How do you calculate the correlation coefficient for a dataset?
2. What does the slope of a regression equation represent in a practical scenario?
3. How do residuals affect the accuracy of a regression model?
4. What are the assumptions underlying linear regression analysis?
5. Can you calculate a confidence interval for the regression parameters?

Tip: In regression analysis, always check for outliers, as they can significantly skew the results!