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Please type out the problem; you are not supposed to post thumbnails. (Read Vela's 'pinned' post called 'Guidelines for students and helpers', especially topic 4.) This morning I could not read your thumbnail on the medium I was using then.gxc9800 said:Homework Statement
i have problem of finding the degree f freedom for this question. the ans for v is 3 , but my ans is v=n-1 , where n = 6 , so my v=5...
Homework Equations
The Attempt at a Solution
the question contains boxes. if i type out , it would look weird... so i would rather post the image of the original questionRay Vickson said:Please type out the problem; you are not supposed to post thumbnails. (Read Vela's 'pinned' post called 'Guidelines for students and helpers', especially topic 4.) This morning I could not read your thumbnail on the medium I was using then.
thanks! question solved!RUber said:Did combining the lower frequency observations solve your problem getting to v=3?
I think the general rule is frequency ## \geq## 5.
The CHI square test is used to determine whether there is a significant difference between observed and expected values in a categorical data set. It helps to determine if any observed differences are due to chance or if there is a true relationship between the variables being studied.
The degree of freedom in a CHI square test is calculated by subtracting 1 from the number of categories in the data set. For example, if there are 4 categories, the degree of freedom would be 4-1 = 3.
The degree of freedom helps to determine the critical value of CHI square, which is used to determine the significance of the test. It also affects the shape of the CHI square distribution and the accuracy of the results.
A higher degree of freedom indicates that there are more categories in the data set, making the test more sensitive and accurate. It also means that there is a larger sample size, which can lead to more reliable results.
No, the degree of freedom cannot be negative in a CHI square test. It is always a positive value as it represents the number of independent pieces of information that are used to estimate a parameter. If the calculated degree of freedom is negative, it is most likely an error in calculation.