100 Statistical Tests by Gopal K Kanji

By Gopal K Kanji

This extended and up-to-date 3rd version of Gopal okay. Kanji’s best-selling source on statistical assessments covers the entire most typically used checks with details on find out how to calculate and interpret effects with uncomplicated datasets. every one access starts with a brief precis assertion concerning the test’s goal, and includes info of the try target, the constraints (or assumptions) concerned, a quick define of the tactic, a labored instance, and the numerical calculation. a hundred Statistical exams, 3rd variation is the only imperative advisor for clients of statistical fabrics and shoppers of statistical info in any respect degrees and throughout all disciplines.

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96, so we do not reject the null hypothesis. That is, there is no reason to suggest, for this sample, that the filling process is not running on target. On the other hand if we are only concerned about over-filling of the cosmetic then a one-tailed test is appropriate. 645. 05 (or 10 per cent) whether the test is one- or two-tailed. So now we reject the null hypothesis and can reasonably suspect that we are over-filling the tubs with cosmetic. Quality control inspectors would normally take regular small samples to detect the departure of a process from its target, but the basis of this process is essentially that suggested above.

66 [Table 7]. Hence the null hypothesis should not be rejected. Here u1 = 53 54 Test 21 100 STATISTICAL TESTS Dixon’s test for outliers Object To investigate the significance of the difference between a suspicious extreme value and other values in the sample. Limitations 1. The sample size should be greater than 3. 2. The population which is being sampled is assumed normal. Method Consider a sample of size n, where the sample is arranged with the suspect value in front, its nearest neighbour next and then the following values arranged in ascending (or descending) order.

Two sales teams responsible for essentially identical products are compared by selecting samples from each and reviewing their respective profit contribution levels per employee over a period of two weeks. 3 respectively. How do the two teams compare on performance? 72. 26 so we reject the null hypothesis and accept the alternative. There is a significant difference between the two teams. Team 1 is more productive than team 2. 26 [Table 2]. Reject the null hypothesis. THE TESTS 35 Test 10 t-test for two population means (method of paired comparisons) Object To investigate the significance of the difference between two population means, µ1 and µ2 .

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