Linear Regression
Linear Regression. Fit the line y = mx + b to linear data where:
- x is the dependent variable
- y is the independent variable
- xi is the x value for i'th data point
- yi is the y value for the i'th data point
- N is the number of different standards are used
- yave is the average of the y values for the standards
- xave is the average of the x values for the standards.
This method assumes that there is no variance in the value for x and that each standard is analyzed once.
- Calculate Sums:
![](gif/fig14.gif)
![](gif/fig15.gif)
![](gif/fig16.gif)
- Calculate Slope and Intercept:
![](gif/fig17.gif)
![](gif/fig18.gif)
- Uncertainty in Regression. Assuming linear function and no replicates, the standard deviation about the regression is:
![](gif/fig19.gif)
- Uncertainty in ypredicted.
![](gif/fig20.gif)
- Uncertainty in xpredicted. For an unknown with an average signal yunk from M replicates:
![](gif/fig21.gif)