Texas Instruments TINSPIRE Teacher Software Guidebook - Page 597

1-Prop z Test (zTest_1Prop) computes a test for an unknown proportion of successes (prop). It takes as input the count of successes in the sample x and the count of observations in the sample n. 1-Prop z Test tests the null hypothesis H0: prop=p0 against one of the alternatives below. • Ha: propƒp0 • Ha: propp0 This test is useful in determining if the probability of the success seen in a sample is significantly different from the probability of the population or if it is due to sampling error, deviation, or other factors. 2-Prop z Test (zTest_2Prop) computes a test to compare the proportion of successes (p1 and p2) from two populations. It takes as input the count of successes in each sample (x1 and x2) and the count of observations in each sample (n1 and n2). 2-Prop z Test tests the null hypothesis H0: p1=p2 (using the pooled sample proportion Ç) against one of the alternatives below. • Ha: p1ƒp2 • Ha: p1p2 This test is useful in determining if the probability of success seen in two samples is equal. c2GOF (c2GOF) performs a test to confirm that sample data is from a population that conforms to a specified distribution. For example, c2 GOF can confirm that the sample data came from a normal distribution. c2 2-way Test (c22way) computes a chi-square test for association on the two-way table of counts in the specified Observed matrix. The null hypothesis H0 for a two-way table is: no association exists between row variables and column variables. The alternative hypothesis is: the variables are related. 2-Sample FTest (FTest_2Samp) computes an F-test to compare two normal population standard deviations (s1 and s2). The population means and standard deviations are all unknown. 2-Sample FTest, which uses the ratio of sample variances Sx12/Sx22, tests the null hypothesis H0: s1=s2 against one of the alternatives below. Using Lists & Spreadsheet 585