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Q: How do you find degrees of freedom for a chi-squared test?

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One less than the possible outcomes.

A library, or any bookshop

If you got 20 problems wrong on a 75 question test your grade would be 73% or a C. You can find that by subtracting the amount of questions you got wrong from the amount you got right, 55 in this case. Divide the amount of correct questions by the amount of questions on the test to get .733333. That is the grade.

most odd answers are in the back but some like the standardized test practice are not

Get everything right on the test.

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(r-1)x(c-1)

so

If the sample consisted of n observations, then the degrees of freedom is (n-1).

A T test is used to find the probability of a scenario given a specific average and the number of degrees of freedom. You are free to use as few degrees of freedom as you wish, but you must have at least 1 degree of freedom. The formula to find the degrees of freedom is "n-1" or the population sample size minus 1. The minus 1 is because of the fact that the first n is not a degree of freedom because it is not an independent data source from the original, as it is the original. Degrees of freedom are another way of saying, "Additional data sources after the first". A T test requires there be at least 1 degree of freedom, so there is no variability to test for.

The data sets determine the degrees of freedom for the F-test, nit the other way around!

It depends on the degrees of freedom for the f-test.

One less than the possible outcomes.

Critical values of a chi-square test depend on the degrees of freedom.

There are 24 df.

The degrees of freedom for any contingency table can be calculated simply by the formula (r-1)x(c-1) where r= the number of rows and c= the number of columns. Thus for a contingency table with four rows and four columns the degrees of freedom are 3x3 = 9.

Because under the null hypothesis of no difference, the appropriate test statistic can be shown to have a t-distribution with the relevant degrees of freedom. So you use the t-test to see how well the observed test statistic fits in with a t-distribution.

North you can find this out if you look on a world map and find the 30 degrees north line then see if Japan is north or south of it .That's how you could figure it out if you have it on a test.

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