Yes.
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
Given Z~N(0,1), Z^2 follows χ_1^2 Chi-square Probability Distribution with one degree of freedom Given Z_i~N(0,1), ∑_(i=1)^ν▒Z_i^2 follows χ_ν^2 Chi-square Probability Distribution with ν degree of freedom Given E_ij=n×p_ij=(r_i×c_j)/n, U=∑_(∀i,j)▒(O_ij-E_ij )^2/E_ij follows χ_((r-1)(c-1))^2 Chi-square Probability Distribution with ν=(r-1)(c-1) degree of freedom Given E_i=n×p_i, U=∑_(i=1)^m▒(O_i-E_j )^2/E_i follows χ_(m-1)^2 Chi-square Probability Distribution with ν=m-1 degree of freedom
five; they are: position, orientation, shape, and scale
A "bell" shape.
The standard normal distribution or the Gaussian distribution with mean 0 and variance 1.
It is not negative. it is positively skewed, and it approaches a normal distribution as the degrees of freedom increase. Its shape is NEVER based on the sample size.
As the value of k, the degrees of freedom increases, the (chisq - k)/sqrt(2k) approaches the standard normal distribution.
Characteristics of the F-distribution1. It is not symmetric. The F-distribution is skewed right. That is, it is positively skewed.2. The shape of the F-distribution depends upon the degrees of freedom in the numerator and denominator. This is similar to the distribution and Student's t-distribution, whose shape depends upon their degrees of freedom.3. The total area under the curve is 1.4. The values of F are always greater than or equal to zero. That is F distribution can not be negative.5. It is asymptotic. As the value of X increases, the F curve approaches the X axis but never touches it. This is similar to the behavior of normal probability distribution.
Given Z~N(0,1), Z^2 follows χ_1^2 Chi-square Probability Distribution with one degree of freedom Given Z_i~N(0,1), ∑_(i=1)^ν▒Z_i^2 follows χ_ν^2 Chi-square Probability Distribution with ν degree of freedom Given E_ij=n×p_ij=(r_i×c_j)/n, U=∑_(∀i,j)▒(O_ij-E_ij )^2/E_ij follows χ_((r-1)(c-1))^2 Chi-square Probability Distribution with ν=(r-1)(c-1) degree of freedom Given E_i=n×p_i, U=∑_(i=1)^m▒(O_i-E_j )^2/E_i follows χ_(m-1)^2 Chi-square Probability Distribution with ν=m-1 degree of freedom
The shape of a t distribution changes with degrees of freedom (df). As the the df gets very large the shape of the t distribution will begin to look similar to that of a normal distribution. However, the t distribution has more variability than a normal distribution; especially when the df are small. When this is the case the t distribution will be flatter and more spread out than the normal distributions.
five; they are: position, orientation, shape, and scale
A degree of freedom, is merely a direction (including philosophic) in which an object is not constrained. In our usual 3 - dimension geometry, there is yet no constraint on any of the several rotations - these could be considered degrees of freedom.
A "bell" shape.
A skewed bell shape.
the normal distribution is a bell shape and expeonential is rectangular
The distribution of the sample mean is bell-shaped or is a normal distribution.
The whole shape has 540 degrees in it. To work out the amount of degrees in any shape - total degrees in shape = ((180 x number of sides of shape) - 360)