Chi square test goodness of fit- hypothesis testing, Operation Research

Chi Square  Test Goodness of Fit

Chi square test  can be used to find out  how well the  theoretical distribution fit  with the  empirical distribution  of observed distribution obtained from  sample data. When chi square test is used as a  test of goodness of fit you will  be taking  the followings steps:

  • Set up null and alternative hypothesis.
  • Decide level of significance for rejection of null hypothesis.
  • Draw a random sample of observations from the relevant population.
  • Derive theoretical distribution under the assumption that null hypothesis is true.
  • Compare observed frequencies with expected frequencies with the help of test.
  • If computed X2 is less than the table value at a certain level of significance the fit the is considered to be good. On the other hand if the calculated value exceeds the table value the fit is considered poor.


Posted Date: 2/26/2013 11:33:40 PM | Location : United States

Related Discussions:- Chi square test goodness of fit- hypothesis testing, Assignment Help, Ask Question on Chi square test goodness of fit- hypothesis testing, Get Answer, Expert's Help, Chi square test goodness of fit- hypothesis testing Discussions

Write discussion on Chi square test goodness of fit- hypothesis testing
Your posts are moderated
Related Questions
Q. Explain Research Terminology? Research Terminology - Independent and Dependent variables: There are many practical problems in which the values of one variable depend upon t

Trade Literature: Trade literature consists of documents that give information on the processes and materials involved in the manufacture of a product, various types of produc


which job should proceed first among the 2 jobs on n machines by graphical method

what are the limitations of north west corner method

A paper mill produces two grades of paper viz., X and Y. Because of raw material restrictions, it cannot produce more than 400 tons of grade X paper and 300 tons of grade Y paper i

Disadvantages of Mode a.It is  ill defined. b.It is  indefinite  and it is  some cases impossible  to find a definite  value. c.It is not based on all observation. So  i

Making Decision Lastly a decision  should  be arrived as to whether the null  hypothesis is  to be accepted  or rejected. In  this regard the value  of the test  statistic

Discus and explain both probability and none probability sampling techniques

Demerits a.It cannot  be used in  the case  of  bi variate distribution. b.If the  numbers  of items  are greater than say  30, the calculation becomes  tedious  and  requ