By Gopal K Kanji

ISBN-10: 1412923751

ISBN-13: 9781412923750

ISBN-10: 1847878261

ISBN-13: 9781847878267

ISBN-10: 8178297310

ISBN-13: 9788178297316

This increased and up to date 3rd variation of Gopal ok. Kanji’s best-selling source on statistical exams covers all of the most typically used assessments with details on how you can calculate and interpret effects with basic datasets. every one access starts with a brief precis assertion concerning the test’s function, and comprises information of the try aim, the restrictions (or assumptions) concerned, a short define of the strategy, a labored instance, and the numerical calculation. a hundred Statistical assessments, 3rd variation is the single necessary consultant for clients of statistical fabrics and shoppers of statistical info in any respect degrees and throughout all disciplines.

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**Extra resources for 100 Statistical Tests **

**Example text**

96, we reject the null hypothesis and conclude that there is significant difference between the mechanisms in terms of accuracy. The second mechanism is significantly less accurate than the first. 96 [Table 1]. Reject the null hypothesis. 28 100 STATISTICAL TESTS Test 6 Z-test for comparing two counts (Poisson distribution) Object To investigate the significance of the difference between two counts. Limitations The test is approximate and assumes that the counts are large enough for the normal approximation to the Poisson to apply.

The test is approximate if the populations are normally distributed or if the sample sizes are sufficiently large. 3. The test should only be used to test the hypothesis µ1 = µ2 . Method Consider two populations with means µ1 and µ2 . Independent random samples of size n1 and n2 are taken from which sample means x¯ 1 and x¯ 2 and variances n1 n2 (xi − x¯ 1 )2 s12 = i=1 n1 − 1 (xi − x¯ 2 )2 and s22 = i=1 n2 − 1 are calculated. The test statistic is t= (¯x1 − x¯ 2 ) − (µ1 − µ2 ) s2 s12 + 2 n1 n2 1 2 which may be compared with Student’s t-distribution with degrees of freedom given by ⎫ ⎧ 2 2 2 ⎪ ⎪ s s ⎪ ⎪ 1 ⎪ ⎪ ⎪ ⎪ + 2 ⎪ ⎪ ⎬ ⎨ n1 n2 .

The test statistic Z= (¯x1 − x¯ 2 ) − (µ1 − µ2 ) σ 1 1 + n1 n2 1 2 may be compared with the standard normal distribution using either a one- or two-tailed test. Example Two teams of financial sales persons are compared to see if it is likely that the instruction each has received could have led to differing success rates. 2. 7. 4405). The success rate is calculated using a range of output measures for a transaction. If we are only interested to know of a difference between the two teams then a two-tailed test is appropriate.

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