To calculate the error (or uncertainty) for the slope of a linear regression, we can follow these steps:

Steps:

1. Perform a linear regression to determine the slope of the line. In your case, the relationship between the standard concentrations (ppm) and the peak areas (a.u.) can be fitted with a linear model.

2. Calculate the error in the slope. The standard error of the slope can be determined using statistical formulas after fitting the regression model. This will involve:

   • Calculating the residuals (differences between observed and predicted values).
   • Determining the standard deviation of the residuals.
   • Using the formula for the standard error of the slope:

   $SE_{\text{slope}} = \frac{\sigma}{\sqrt{\sum (x_i - \bar{x})^2}}$ where $\sigma$ is the standard deviation of the residuals, and $x_i$ represents the independent variable (concentration in ppm), and $\bar{x}$ is the mean of the independent variable.

Let me calculate the slope and error from the data you provided.

I'll first compute the linear regression and then calculate the error for the slope.The slope of the linear regression is approximately $135.60$ (a.u./ppm), and the standard error of the slope is approximately $2.60$. This means the uncertainty in the slope is around $\pm 2.60$.

Would you like more details or have any further questions?

Here are some related questions:

1. How is the standard error of the slope interpreted in terms of data confidence?
2. What other statistical values can be derived from a linear regression analysis?
3. How does the correlation coefficient (R-value) reflect the fit of the data?
4. Can we compute the error for predicted peak areas based on this linear fit?
5. How can outliers affect the slope and its error?

Tip: The lower the standard error, the more reliable the slope estimation is. It indicates less variability in the data around the regression line.