# 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 