To calculate the value of $s_e$, which represents the standard error of the estimate (the standard deviation of the residuals), we can use the formula:

$s_e = \sqrt{\frac{ \sum (y_i - \hat{y_i})^2 }{n - 2}}$

Where:

• $y_i$ are the observed values (the percentages of salmon transported),
• $\hat{y_i}$ are the predicted values from the regression equation, and
• $n$ is the number of data points (in this case, the number of rows of data).

From the Minitab output:

• $R^2 = 83.2\%$, and
• The residual standard error $S = 9.16217$.

The residual standard error $S$ is actually the value of $s_e$, which represents the standard error of the estimate.

So, the value of $s_e$ is:

$s_e = 9.16217$

Please confirm if you need additional details or clarifications. Here's a list of related questions that might help deepen understanding:

1. What is the interpretation of the coefficient for the total number of salmon in the regression equation?
2. How do you calculate the predicted percentage of salmon transported for a given total number using the regression equation?
3. How does the $R^2$ value reflect the quality of the regression model?
4. What does it mean if the standard error of the estimate is large or small in a regression analysis?
5. How do residuals impact the validity of a regression model?

Tip: Always check whether the assumptions of linear regression (such as linearity, independence, and homoscedasticity) hold before drawing conclusions from the regression model.