A high F statistic would results in a lower Sig, or P value, which would indicate that your results are significant.
the populations have an excess of heterozygotes
No The test statistic F-Test is a sum of squares, which by definition of squaring a number it must be positive.
An F-statistic is a measure that is calculated from a sample. It is a ratio of two lots of sums of squares of Normal variates. The sampling distribution of this ratio follows the F distribution. The F-statistic is used to test whether the variances of two samples, or a sample and population, are the same. It is also used in the analysis of variance (ANOVA) to determine what proportion of the variance can be "explained" by regression.
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
A 3 mile loop, by itself, cannot have a F-statistic.
Ib
A high F statistic would results in a lower Sig, or P value, which would indicate that your results are significant.
the populations have an excess of heterozygotes
Usually the F-statistic.
The parent probability distribution from which the statistic was calculated is referred to as f(x) and cumulative distribution function as F(x). The sampling distribution and cumulative distribution of a statistic is commonly referred to as g(y) and G(y) where Y is the random variable representing the statistic. There are numerous other notations.
No The test statistic F-Test is a sum of squares, which by definition of squaring a number it must be positive.
Not in itself. You need to say what it is. Perhaps it's an F statistic?
F is the test statistic and H0 is the means are equal. A small test statistic such as 1 would mean you would fail to reject the null hypothesis that the means are equal.
Mean, variance, t-statistic, z-score, chi-squared statistic, F-statistic, Mann-Whitney U, Wilcoxon W, Pearson's correlation and so on.
An F-statistic is a measure that is calculated from a sample. It is a ratio of two lots of sums of squares of Normal variates. The sampling distribution of this ratio follows the F distribution. The F-statistic is used to test whether the variances of two samples, or a sample and population, are the same. It is also used in the analysis of variance (ANOVA) to determine what proportion of the variance can be "explained" by regression.
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.