Problem understanding number of degrees of freedom

In summary, the conversation discusses degrees of freedom in a classical mechanics course, specifically for a system of point masses and a rigid body. It is mentioned that a rigid body has 6 degrees of freedom, but when constraints are added to a 4 point mass system, the formula gives 7 degrees of freedom. However, it is understood that this is because the constraints do not fully constrain the object in 3 dimensions. To fully constrain the arrangement, another fixed angle or distance would need to be added.
  • #1
UniPhysics90
16
0
Hi,

I do a classical mechanics course, and part of it relates to degrees of freedom.

For a system M of point masses with j constraints, there is N=3M-j DoF

For a rigid body, I know there is 6 DoF (3 translational, 3 rotational).

However, I've tried using drawing the constraints on a 4 point mass system to make it a rigid system. I've done this with 5 constraints (3 rigid rods in blue and 2 fixed angles in green). With 5 constraints the formula gives N=(3x4)-5=7 DoF.

Could someone explain this please?

[PLAIN]http://img152.imageshack.us/img152/7426/degreesoffreedom.png

It'd be greatly appreciated. Thanks

EDIT: I think I understand this now, because it's a rigid body, you can work out all other positions in the system itself from the 5 given constraints. All that you can't work out, is the rotational and translational DoF of the system as a whole.
 
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  • #2
The figure you have drawn does have 7 degrees of freedom. The "constraints" (rods) that you've placed between the points do not fully constrain the object in 3 dimensions---you can twist the vertical and horizontal bars around the diagonal bar (if that makes sense).

In other words, there are 3x translation, 3x rotation, and also an undefined angle between one of the green angles and the plane of the other. To fully constrain the arrangement of your figure, you would have to add another fixed angle, or fixed distance.
 

What is the concept of degrees of freedom in statistics?

Degrees of freedom in statistics refers to the number of independent observations or parameters that can vary in a data set without affecting the overall results. It is a measure of the amount of information available in a data set.

Why is it important to consider degrees of freedom when analyzing data?

Degrees of freedom play a crucial role in determining the validity and reliability of statistical analyses. It helps prevent overestimating the significance of results and ensures that the conclusions drawn from the data are accurate.

How do you calculate degrees of freedom?

The formula for calculating degrees of freedom depends on the statistical test being performed. For example, in a t-test, the degrees of freedom are determined by the sample size, while in ANOVA, it is calculated by subtracting the number of groups from the total number of observations.

What happens if the degrees of freedom are too low?

If the degrees of freedom are too low, it can result in inaccurate and unreliable statistical results. This can happen when the sample size is too small or when there are too many variables in the analysis compared to the sample size.

Can degrees of freedom be negative?

No, degrees of freedom cannot be negative. It is a theoretical concept that represents the number of observations or parameters that can vary in a data set. Therefore, it can only be a positive integer or zero.

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