Chic-square goodness-of-fit test

Chic-square goodness-of-fit test

 

Chic-square goodness-of-fit test aims at testing the significance difference between observed distribution and expected. This type of test intends to investigate categorical statistics. The test is used to investigate whether a given sample of data is dependent to the hypothesized distribution. In other words, data has to be categorized, since it cannot analyze parametric or even continuous statistic like heights. In addition, chic-square test of goodness is used to establish how a given distribution estimates another.  On the other hand, independent t test is used in cases where one wants to compare the mean of normally distributed variables such as determining whether the mean height for female and male of a given sample is similar (Bruin, 2006).

To some degree, chic-square goodness of fit is similar to independent t-test because only two variables are used. It is not similar to independent t-test because Chic-square goodness-of-fit, uses frequency that are mostly discrete variables while independent t test involves parametric or continuous variables (Bruin, 2006).  This test can be used to analyze gender. For instance, a school with 35%or 10 male and 65 % or 14 female. The specifics of this analysis are to get nominal variable (gender), current group (24 students) and comparing the two frequencies. Two significant theories are used: observed frequency as well as anticipated frequency. Experimental rate of recurrence is the count of observations for the present group- in this regard, the 24 students. Anticipated frequency is the tot up of frequencies in the assessment group- all secondary school learners.  The difference of observed frequencies as well as anticipated frequencies is known as the chi-square review. This form of analysis is normally represented using a cross-tabulation figure.

References

Bruin, J. (2006). Newest: command to compute new test.  UCLA:                                                                                                                                                                                                                                                                                                                                                                                                                                               Statistical Consulting Group.  http://www.ats.ucla.edu/stat/stata/ado/analysis/.

 

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