# Table 1

Term Explanation
Cook's Distance measures the influence of a particular data point on all the other data points in a linear regression, it indicates how important a particular data point is for the method 
F ratio of the variance or mean square between groups to the variance within groups
Linear Regression where one variable is expressed as a function of another variable in a statistical analysis using simple least squares methods
log-log plot double logarithm plot, if y = cxa, where x is the independent variable, c is a constant and a is an exponent, then logy = alogx + logc and the slope of the resulting line is the exponent a. An exponent of 2 would imply a square or quadratic relationship while an exponent of 0.5 would imply a square root relationship between the variables
median half the values in a distribution are higher and half the values are lower than the median value
P value with a null hypothesis of no difference between two or more samples, the P value is the probability that the null hypothesis is true, and that the observed difference is due to a chance event
Quantiles of the standard normal QQplot. Plot of data against the corresponding quantiles of a standard normal distribution, one with a mean of zero and a variance of one. If the plot is fairly linear, the data are reasonably Gaussian or normal 
R2 the square of the correlation coefficient. It is an estimate of the variance explained by a particular statistical model
Robust Regression the robust fit is minimally influenced by outliers in the data, minimizing bias in the estimates of the coefficients [15, 16]
s.e. standard error, which is an estimate of the accuracy of a mean (s.e.m.) or other coefficient given the variability found in a particular set of data; it is fundamental to understanding whether two means are likely to be from the same or from different distributions
t calculated from the difference between means divided by the standard error of the difference between two means (Student's t-test) and in ordinary least-squares regression analyses to determine whether a slope is significantly different from zero by comparing the slope to its standard error 