Chi Square Goodness Of Fit Test

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Chi- Square Goodness of Fit Testing Assuming Equal Expected
A handful of students loves numbers and selects a major in Finance, Statistics, or Marketing. While others prefer a major in Leadership or are undecided. Historically, the MBA program at Whatsamatta U has about 40% of their students electing Leadership as their major, 30% selecting Finance as a major, 20% choosing Marketing, and 10% are undecided. Conducting a Chi-Square Goodness of the Fit Test revealed that the class of 200 students does not fit the historical pattern when using a .05 significance level. Therefore, rejecting the null hypothesis. One must establish a comprehension of Chi-Square Testing. The Chi-Square Goodness of Fit Test are calculations done on categorical data.
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Where the two points intercept on the table, it gives the critical value. 7.815 21
After doing the manual calculations and bell graph, the data indicates the matching numbers with the Excel spreadsheet. The Chi-square Test Statistic shows 21 far from 7.815. The p-value indicates 0.0001 or .01% error at .05 or 5%. The data emerges a powerful and concrete report.
Recall, the percentages were ignored at the beginning for the equal expectancy of 50. Additionally, the calculation is done just the same as the Chi-Square of Goodness of Fit Testing but with assuming unequal expected. The process is similar the only difference is to include the correct percentages with the corresponding majors as indicated. Should an individual what to do the standard calculation one can do that my creating a frequency table. The manual calculation is x^2=∑(0-E)^2/E . Table of Frequency: The expected is calculated by the number of % out of 200 students. Leadership 40%, Finance 30%, Marketing 20%, and No Major
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Where the two points intercept on the table, it gives the critical value. 7.815 52.28
In summary, the numbers revealed the T-Test of 52.28 is in the rejection zone. Nerveless, causing a conclusion of the Ha being correct. The majors of the most recent class of 200 students do not fit the same pattern causing a shift in the choices of majors. Moreover, the rejecting of the null hypothesis. What does the two Chi-Square of Goodness of Fit Test (Assuming Equal Expected) and (Assuming Unequal Expected) they both give a person the same information in rejecting the null hypothesis.
References
Faulkner, G.P., Livingstone, M.E., McCaffrey, T.A., & Kerr, M.A. (2014) Supermarket own brand foods: lower in energy cost but similar in nutritional quality to their market brand alternatives. Journal of Human Nutrition & Dietetics, 27 (6) 617-625. doi:

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