The derivative of the moment generating function is the expectation. The variance is the second derivative of the moment generation, E(x^2), minus the expectation squared, (E(x))^2.
ie var(x)=E(x^2)-(E(x))^2
:)
the variance of the uniform distribution is (a+b)/12
The independent variable explains .32*100 percent of the variance in the dependent variable.This is 9%.The explainable variance is always the square of the correlation (r).
It means that the variance remains the same across the range of values of the variable.
Concomitant variance refers to the situation where the variability of one variable is related to the variability of another variable. It indicates that as one variable changes, the degree of variability in another variable also changes, suggesting a potential relationship between the two. This concept is often used in statistics and research to understand how different factors may influence each other's variability. Understanding concomitant variance can help in identifying interactions in data and improving model predictions.
It is exp(20t + 25/2*t^2).
Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.
Variable overhead cost variance is that variance which is in variable overheads costs between the standard cost and the actual variable cost WHILE fixed overheads cost variance is variance between standard fixed overhead cost and actual fixed overhead cost.
efficiency variance, spending variance, production volume variance, variable and fixed components
the variance of the uniform distribution is (a+b)/12
The independent variable explains .32*100 percent of the variance in the dependent variable.This is 9%.The explainable variance is always the square of the correlation (r).
To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.
It means that the variance remains the same across the range of values of the variable.
The unaccounted for variance aka Error Variance, is the amount of variance of the dependent variable (DV) that is not accounted for by the main effects/independent variables (IV) and their interactions.
The coefficient of simple determination tells the proportion of variance in one variable that can be accounted for (or explained) by variance in another variable. The coefficient of multiple determination is the Proportion of variance X and Y share with Z; or proportion of variance in Z that can be explained by X & Y.
Let me guess...stats comps, too?
23.18
independent or quasi-independent variable