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There is no direct relationship between degrees of freedom and probability values.
Mass and damping are associated with the motion of a dynamic system. Degrees-of-freedom with mass or damping are often called dynamic degrees-of-freedom; degrees-of-freedom with stiffness are called static degrees-of-freedom. It is possible (and often desirable) in models of complex systems to have fewer dynamic degrees-of-freedom than static degrees-of-freedom.
a superstructure has negative degree of freedom... ;0
To calculate the degrees of freedom for a correlation, you have to subtract 2 from the total number of pairs of observations. If we denote degrees of freedom by df, and the total number of pairs of observations by N, then: Degrees of freedom, df=N-2. For instance, if you observed height and weight in 100 subjects, you have 100 pairs of observations since each observation of height and weight constitutes one pair. If you want to calcualte the correlation for these two variables (height and weight), your degrees of freedom would be calculated as follows: N=100 df=N-2 Therefore, df=100-2=98 The degrees of freedom are a function of the parameters; you subtract the amount of parameters free to vary from the n to get the df, so logically in a correlation we should subtract 2 from n, as we are looking at a correlation between 2 variables.
Yes. The parameters of the t distribution are mean, variance and the degree of freedom. The degree of freedom is equal to n-1, where n is the sample size. As a rule of thumb, above a sample size of 100, the degrees of freedom will be insignificant and can be ignored, by using the normal distribution. Some textbooks state that above 30, the degrees of freedom can be ignored.