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 [14] |
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 [16] |
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 |