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The degrees of freedom is a count of the number of variables which are free to vary. This number can be fewer than the number of variables because of constraints imposed by additional information. Suppose, for example, you have n variables and you also know their mean. Knowing the mean is effectively the same as knowing their sum. So (n-1) of the variables can take any value but, after that, the nth variable must be such that all n of them add to the know sum.
Similarly, if you have n variables and are given k means (for k classes), then only n-k of the variables are free to take any value. There are k constraints imposed by the k means.
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What are the difference between static degrees of freedom and dynamic degrees of freedom?
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.
If a related measures experiment and matched subject experiment both produce t statistics with degrees of freedom of 20 which experiment used more subjects?
The matched subject because the experiment involves pairs which halves the df.
Why there is no degrees of freedom in Z test?
so
What is the degree of freedom is the probability is 0.05?
There is no direct relationship between degrees of freedom and probability values.
How many degrees of freedom in two dimension?
Two.
