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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.

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What is the degree of freedom is the probability is 0.05?

There is no direct relationship between degrees of freedom and probability values.


When testing for differences between two means the sample population are?

You are testing the difference between two means of independent sample and the population variance are not known. from those population you take two samples of two different size n1and n2. what degrees of freedom is appropriate to consider in this case


How many degrees of Freedom is the universal joint?

A universal joint has two degrees of freedom. It allows for rotational movement around two axes, enabling the connected shafts to pivot in different directions. This flexibility helps accommodate misalignments between the shafts while transmitting torque.


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so


How do you calculate degrees of freedom for a correlation?

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.

Related Questions

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