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Cook's Distance
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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]
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F
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ratio of the variance or mean square between groups to the variance within groups
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Linear Regression
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where one variable is expressed as a function of another variable in a statistical analysis using simple least squares methods
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log-log plot
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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
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median
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half the values in a distribution are higher and half the values are lower than the median value
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P value
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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
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Quantiles of the standard normal
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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]
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R2
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the square of the correlation coefficient. It is an estimate of the variance explained by a particular statistical model
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Robust Regression
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the robust fit is minimally influenced by outliers in the data, minimizing bias in the estimates of the coefficients [15, 16]
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s.e.
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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
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t
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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
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