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Examples of hypothesis testing in statistics, focusing on left-tailed and right-tailed tests. It includes problems related to vitamin c intake in pregnant women and unemployment rates, demonstrating how to formulate null and alternative hypotheses and determine z-values based on significance levels. The document also explains when to reject the null hypothesis based on the z-score. It is useful for students learning basic statistical inference and hypothesis testing concepts, providing practical examples and clear explanations of key steps.
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It is recommended that pregnant women over eighteen years old get 85 milligrams of vitamin C each day. A doctor is concerned that her pregnant patients are not getting enough vitamin C. So, she collects data on 35 of her patients and finds that the mean vitamin intake of these 35 patients is 82 milligrams per day with a standard deviation of 16 milligrams per day. Based on a level of significance of α = .025, test the hypothesis. Ho: u = 85 H1: u < 85 ---> left tailed because LESS THAN u = 85 n = 35 xbar = 82 H1: u < 1020 a = 0.04 ---- > look on standard normal distribution chart for left, 0.04006 is closest value to 0.04, z =
Left tailed test z = - 1.
Suppose that we have a problem for which the null and alternative hypothesis are given by: H 0 : μ=1020. H 1 :μ< 1020. Is this a right-tailed test, left-tailed test, or two-tailed test. Find the z value based on a level of significance of .04. Left-tailed test. P(Z< p=""><> This corresponds to z= - 1..