the populations have an excess of heterozygotes
The F statistic is statistic which may be used to test whether a regression accounts for a statistically significant proportion of the observed variation in the dependent variable.
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 high z-score (or t-score, depending on what info you've been given for the data) means that a number is very far away from the mean (average) number. This number might be an outlier.
No The test statistic F-Test is a sum of squares, which by definition of squaring a number it must be positive.
the populations have an excess of heterozygotes
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.
A 3 mile loop, by itself, cannot have a F-statistic.
The F statistic is statistic which may be used to test whether a regression accounts for a statistically significant proportion of the observed variation in the dependent variable.
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.
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.
Ib
If X and Y have Gaussian (Normal) distributions, then the ratio ofthe mean of m variables distributed as X2 andthe mean of n variables distributed as Y2 hasan F distribution with m and n degrees of freedom.
On November 2, 2010, in New York City, the actual high temperature was 50 degrees Fahrenheit (10 degrees Celsius), the actual low temperature was 36 F (2 C), and the actual mean temperature was 43 F (6 C). The average high temperature was 58 F (14 C), the average low temperature was 45 F (7 C), and the average mean temperature was 51 F (11 C). On November 3, the actual high temperature was 54 F (12 C), the actual low temperature was 41 F (5 C), and the actual mean temperature was 48 F (9 C). The average high temperature was 57 F (14 C), the average low temperature was 45 F (7 C), and the average mean temperature was 51 F (11 C). On November 4, the actual high temperature was 51 F (11 C), the actual low temperature was 47 F (8 C), and the actual mean temperature was 49 F (9 C). The average high temperature was 57 F (14 C), the average low temperature was 44 F (7 C), and the average mean temperature was 51 F (11 C). See the Related Link below for the Weather Underground website, which gives you weather conditions for cities worldwide. Scroll to the middle of the screen to find weather history.
A high z-score (or t-score, depending on what info you've been given for the data) means that a number is very far away from the mean (average) number. This number might be an outlier.
It is a defensive statistic that stands for "Assists"