The Chi- square Test
A chi square (X2) statistic is worn to examine whether distributions of categorical variables differ from one another. Chi Square is in employ to test the variation between a exact sample and an additional hypothetical or earlier documented distribution such as that which may be predictable unpaid to possibility or probability.
A chi square (X2) statistic is worn to examine whether distributions of categorical variables differ from one another. Basically categorical variable acquiesce data in the grouping and numerical variables acquiesce data in numerical form. Rejoinder to such questions as "What is your major?" or do you own a car?" are categorical for the reason that they acquiesce data such as "biology" or "no." Numerical data can be either discrete or continuous.
The Chi Square (X2) test is doubted the majority imperative and the majority used associate of the nonparametric family of statistical tests. Chi Square is in employ to test the variation between a exact sample and an additional hypothetical or earlier documented distribution such as that which may be predictable unpaid to possibility or probability. Chi Square can as well be used to test differences among two or more definite samples.
Chi-square is a statistical test normally used to evaluate experiential data with data we would be expecting to get hold of according to a precise hypothesis. The chi-square test is for eternity testing what scientists identify the null hypothesis, which condition that there is no important dissimilarity between the probable and experiential consequence. The principle for calculating chi-square ( 2) is:
2= (o-e) 2/e
That is, chi-square is the sum of the squared dissimilarity among observed (o) and the predictable (e) data, divided by the probable data in all probable grouping. The chi-square (I) test is used to decide whether there is an important dissimilarity among the expected frequencies and the observed frequencies in one or more categories.
1. Quantitative information.
2. One or more grouping.
3. Sovereign explanation.
4. Sufficient sample mass (at least 10).
5. Simple random sample.
6. Data in occurrence form.
7. All explanation has to be used.
When you discover the charge for chi square, you decide whether the experimental frequencies be different appreciably from the predictable frequencies.
1. You hypothesize that all the frequencies are equal in each category. For example, you might expect that half of the entering freshmen class of 200 at Tech College will be identified as women and half as men. You figure the predictable frequency by dividing the number in the sample by the number of categories.
2. You decide the probable frequencies on the basis of some prior knowledge. Let's use the Tech College illustration again, but this time imagine we have preceding knowledge of the frequencies of men and women in each kind from last year's entering class, when 60% of the freshmen were men and 40% were women.
Freedom 0.9 0.5 0.1 0.05 0.01
1 0.02 0.46 2.71 3.84 6.64
2 0.21 1.39 4.61 5.99 9.21
3 0.58 2.37 6.25 7.82 11.35
4 1.06 3.36 7.78 9.49 13.28
5 1.61 4.35 9.24 11.07 15.09