| Equation of the line |
The relationship between the concentration (X) and the response (Y) |
Y = b0 + b1 × X |
| Intercept (b0) |
The value of Y when X equals zero |
b0 = y − b1 × X |
| Slope (b1) |
The slope of the line relate to the relationship between concentration and response |
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| Standard error (b0) (SE intercept) |
The standard error of the intercept can be used to calculate the required confidence interval |
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95% confidence interval |
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| Standard error (b1) (SE slope) |
The standard error of the slope can be used to calculate the required confidence interval |
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95% confidence interval |
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| Coefficient of determination (r2) |
The square of the correlation coefficient |
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| Correlation coefficient (r) |
The correlation between the predicted and observed values. This will have a value between 0 and 1; the closer the value is to 1, the better the correlation |
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| Regression SS |
The regression sum of squares is the variability in the response that is accounted for by the regression line |
SS total − ∑(Xi)2 |
| Residual SS (the error sum of squares) |
The residual sum of squares is the variability about the regression line (the amount of uncertainty that remains) |
SS total − SS regression |
| Total SS |
The total sum of squares is the total amount of variability in the response |
∑(Yi − Y)2 |
