Inverse Chi Square: Rejecting Null Hypothesis at α=5%, 9DF

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SUMMARY

To reject the null hypothesis at α = 5% with 9 degrees of freedom, one must refer to the chi-square distribution table. The critical value for this scenario is approximately 16.919. The discussion clarifies the distinction between the inverse chi-square distribution and the chi-square statistic, emphasizing the importance of understanding the probability density function (PDF) of the distribution in question. Additionally, numerical routines may be necessary to solve for cumulative probabilities when tables are unavailable.

PREREQUISITES
  • Understanding of chi-square distribution and its properties
  • Familiarity with statistical hypothesis testing
  • Knowledge of inverse chi-square distribution definitions
  • Ability to use statistical tables or numerical routines for probability calculations
NEXT STEPS
  • Study chi-square distribution tables for various degrees of freedom
  • Learn about inverse chi-square distribution and its applications
  • Explore numerical methods for calculating cumulative probabilities
  • Review statistical hypothesis testing techniques and their implications
USEFUL FOR

Statisticians, data analysts, and researchers involved in hypothesis testing and statistical modeling will benefit from this discussion, particularly those working with chi-square statistics and distributions.

MadViolinist
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How can I determine what the smallest value of a χ2 statistic must be to reject the null hypothesis at α = 5%, for a distribution with 9 degrees of freedom? Thanks in advance.
 
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MadViolinist said:
How can I determine what the smallest value of a χ2 statistic must be to reject the null hypothesis at α = 5%, for a distribution with 9 degrees of freedom? Thanks in advance.

It's not clear from your question whether you asking about the inverse chi square distribution or simply asking about how to determine the correspondence between the chi square value and alpha.

In the first case, there are two definitions of the inverse chi square distribution. One is the chi square of 1/X for \nu degrees of freedom and the second is the chi square of \nu / X for \nu degrees of freedom.
 
Hey MadViolinist and welcome to the forums.

Following on from what SW VandeCarr said, do you know the PDF of the distribution you are working with (chi-square if you are using a chi-square statistic) and how you solve (using a numerical routine) the value of a cumulative probability?
 
Hey all:
I just found out that the question I was asking required the use of a table of corresponding X^2 statistics and their probabilities (which I was not given). Thanks for your time anyway.
 

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