Chapter 9 Hypothesis Testing Case Problem 1: Quality Associates, Inc. 1. The hypothesis testing results are shown below: Sample 1 Sample 2 Sample 3 Sample 4 Sample Size 30 30 30 30 Mean 11.959 12.029 11.889 12.081 Standard Deviation 0.220 0.220 0.207 0.206 Level of Significance (alpha) 0.010 0.010 0.010 0.010 Critical Value (lower tail) -2.576 -2.576 -2.576 -2.576 Critical Value (upper tail) 2.576 2.576 2.576 2.576 Hypothesized value 12 12 12 12 Standard Error 0.040
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Inferential Psychology: Hypothesis testing and the search for truth Asking questions of nature has been a part of science from the beginning. In psychology we generally move towards a model of natural science that makes use of inferential hypothesis testing as a central tenet or dogma. As indicated earlier, with respect to sampling from populations, we use a collection of probability distributions and inferential statistics to help us in making decisions about our hypotheses. Essentially
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| |UNIVERSITI TUNKU ABDUL RAHMAN (UTAR) | | | | | |FACULTY OF BUSINESS AND FINANCE (FBF)
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analysis, one practically never has measurements from a whole population and has to infer the characteristics of the population from a sample. Generally speaking parametric methods make more assumptions than non-parametric methods. If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. They are said to have more statistical power. However, if assumptions are incorrect, parametric methods can be very misleading. For that reason they are often not
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the rib cage. The kidneys are vital in that they keep the components of the blood stable, which gives the body a chance to continue working properly (McConnell, 1982). About one to two quarts of urine are produced each day by the kidneys (McConnell, 1982). Urine leaves the kidneys and travels to the bladder through two muscles called ureters. Once the bladder is completely filled with urine, it empties the urine through a tube called the urethra. All of the organs that make up the urinary tract are
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that in practice, a z-test is seldom used, while the ‘default’ test for single sample or two-samples mean(s) is the ttest. This is because in most practical situations, the population variance is seldom known and therefore we need to estimate that by the sample variance, thus justifying a t-test rather than a z-test. It is always good to perform the standard exploratory data analysis before commencing any hypothesis testing involving t-tests. It is often useful to check through summary statistics (like
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and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell
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hypotheses We need to use the z-statistic, which is calculated using Observe that the sample proportion is This corresponds to a two-tailed z-test for proportions. The z-statistics is computed by the following formula: The critical value for for this two-tailed test is. The rejection region is given by Since, then we reject the null hypothesis H0. Hence, we have enough evidence to reject the claim that the true proportion of blue M&Ms® candies
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General Motors Accounting Analysis and Business Solution | | Table of Contents Introduction2 Hypothesis and Methodology 3 Analysis of Problems4 Variables5 Primary and Secondary Sources6 Resources6 Sample………………………………………………………………………………………………………7 Test Statistics……………………………………………………………………………………………….8 Final Recommendations…………………………………………………………………………………….9 Conclusion………………………………………………………………………………………………...10 Appendices………………………………………………………………………………………………..11 Survey……………………………………………………………………………………………………
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percentages? Is a percentage the same thing as a fraction or a proportion? Should we take the difference between two percentages or their ratio? If their ratio, which percentage goes in the numerator and which goes in the denominator? Does it matter? What do we mean by something being statistically significant at the 5% level? What is a 95% confidence interval? Those questions, and much more, are what this book is all about. In his fine article regarding nominal and ordinal bivariate statistics
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