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Pedagogy of chi‐square goodness of fit test for continuous distributions
Author(s) -
Shankar P. Mohana
Publication year - 2019
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22107
Subject(s) - goodness of fit , statistics , statistic , test statistic , statistical hypothesis testing , mathematics , chi square test , test (biology) , permutation (music) , computation , computer science , sample size determination , algorithm , biology , paleontology , physics , acoustics
Chi‐square goodness of fit testing to examine whether or not it is reasonable to assume that a random sample of the data comes from a specific probability density was one of the topics covered in an undergraduate engineering probability course. In the absence of details on this topic in engineering probability books, a Matlab ® demo was created to facilitate the link between theory and practice. The step‐by‐step procedure to determine the closest fit among a number of continuous densities has been demonstrated involving binning (fixed width and fixed population), parameter estimation, and computation of the test statistic, degrees of freedom and the P values. The cautionary aspects of the test regarding the variability in test results have been illustrated by choosing a smaller size data through permutation. The pedagogical aspects of procedure demonstrated suggest that it may be used to fill the gaps in textbooks devoted to probability and statistics.