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Q-test for Rejection of Outliers Introduction The Q-test is a simple statistical test to determine if a data point that is very different from the other data points in a set can be rejected. Only one data point may be discarded using the Q-test. Q = |outlier - value closest to the outlier| / |highest value - lowest value| Table of Q critical values (90% confidence) N 3 4 5 6 7 8 9 10 QC 0.94 0.76 0.64 0.56 0.51 0.47 0.44 0.41 If Q is larger than QC the outlier can be discarded with 90% confidence Linear Regression Introduction Linear regression uses the method of least squares to determine the best equation describing a set of x and y data points. Linear Regression Equations For the equation y = mx + b Useful quantities: Slope: Intercept: Standard deviation of the residuals: Standard deviation of the intercept: Standard deviation of the slope: Standard deviation of a unknown read from a calibration curve: Where: N is the number of calibration data points. L is the number of replicate measurements of the unknown and yc (bar) is the mean of the unknown measurements