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For the test of independence, also known as the test of homogeneity, a chi-squared probability of less than or equal to 0.05 (or the chi-squared statistic being at or larger than the 0.05 critical point) is commonly interpreted by applied workers as justification for rejecting the null hypothesis that the row variable is independent of the ...
A chi-squared test (also chi-square or χ 2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables ( two dimensions of the contingency table ) are independent in influencing the test statistic ...
The general formula for Pearson's chi-squared test statistic is = . The approximation of G by chi squared is obtained by a second order Taylor expansion of the natural logarithm around 1 (see #Derivation (chi-squared) below).
The chi-squared distribution has numerous applications in inferential statistics, for instance in chi-squared tests and in estimating variances. It enters the problem of estimating the mean of a normally distributed population and the problem of estimating the slope of a regression line via its role in Student's t-distribution.
The block chi-square, 9.562, tests whether either or both of the variables included in this block (GPA and TUCE) have effects that differ from zero. This is the equivalent of an incremental F test, i.e. it tests H 0: β GPA = β TUCE = 0. The model chi-square, 15.404, tells you whether any of the three Independent Variabls has significant effects.
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In statistics, minimum chi-square estimation is a method of estimation of unobserved quantities based on observed data. [1]In certain chi-square tests, one rejects a null hypothesis about a population distribution if a specified test statistic is too large, when that statistic would have approximately a chi-square distribution if the null hypothesis is true.
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