Statistical Significance Eric G Peppers HCS/438 Statistical Applications October 8‚ 2012 Gerald Rintala Statistical Significance Identification of a statistic as being significant is more difficult than the novice statistician may at first understand. At the most rudimentary definition‚ a significant finding simply means the statistic is reliable. This term states how convinced you are that a relationship or difference may exist. Bennett‚ et al states‚ “we determine statistical
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Use of Ranks in One-Criterion Variance Analysis Author(s): William H. Kruskal and W. Allen Wallis Source: Journal of the American Statistical Association‚ Vol. 47‚ No. 260 (Dec.‚ 1952)‚ pp. 583-621 Published by: Taylor & Francis‚ Ltd. on behalf of the American Statistical Association Stable URL: http://www.jstor.org/stable/2280779 Accessed: 05-03-2015 13:33 UTC Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use‚ available at http://www.jstor.org/page/info/about/policies/terms
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Research objectives are the goals of the research study. Research objectives specify the reason researchers are conducting research. For example‚ researchers may want to better understand the difference in pain scale results among subjects with a diagnosis of moderate traumatic brain injury. In this case the research objective would cause researchers to take steps to conduct scientific analysis to determine the variations in pain experienced between a group of individuals without a diagnosis of TBI
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ANOVA Hypothesis Test ANOVA Hypothesis Test Living near a major city can be a positive aspect of being a homeowner or someone who uses real estate as an investment. Increasing population contributes to land and space diminishing‚ resulting in high demand for what is available. Industry and markets are in the city‚ attracting buyers who want to have the convenience of living near commercial properties. The difference in the pay scale between jobs in the city and jobs in the suburbs could
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Numerical and Verbal Hypothesis Statement In business‚ small and large or personal the ability to have information to make a decision is essential. “Hypothesis testing is used in both science and business to test assumptions and theories and ultimately guide managers when faced making decisions.” (Doane & Seward‚ 2007‚ page 347‚ Chapter 9). We reviewed and analyzed the data set‚ Real Estate and developed a hypothesis test. The hypothesis test can be described with the following steps: Step 1:
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Worked example from Tutorial 8 8.3 Three laboratories‚ A‚ B‚ and C‚ are used by food manufacturing companies for making nutrition analyses of their products. The following data are the fat contents (in grams) of the same weight of three similar types of peanut butter. | |Laboratory | |Peanut Butter |A |B |C |D | |Brand 1 |16
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Submit completed tests as word or pdf files via email to paul.kurose@seattlecolleges.edu Due: Sunday‚ May 19 (by 8am). 1. a) In Chapter 6‚ you learned to find interval estimates for two population parameters‚ a population mean and a population proportion. Explain the meaning of an interval estimate of a population parameter. An interval estimate for a specified population parameter (such as a mean or proportion) is a range of values in which the parameter is estimated to lie. In Chapter 6
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Tutorial Note 5 • Chapter 17 Hypothesis Testing • Chapter 18 Measures of Association Objective: Computer laboratory session (II) – Please refer to the Computer Lab Notes Discussion Question 1 H0 – Null Hypothesis: a statement you want to reject (E.g Average = 50) H1/HA – Alternative Hypothesis: a statement you want to prove (E.g Average is not 50) What is related/dependent sample? H0: Tutorial is NOT effective H1: Tutorial is effective |Respondent |Before
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Chapter 10 Statistical Inference About Means and Proportions with Two Populations Case Problem: Par‚ Inc. This case can provide discussion and differing opinions as to what hypothesis test should be conducted. Students should begin to see that logical arguments exist for structuring the hypotheses differently. In some interpretations of the problem‚ a two - tailed test can be
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| Statistical Analysis of Yearly Checkups based on Gender | | STAT 3125 Section 04 | | 11/29/2012 | | This statistical analysis is of a randomly selected sample of 300 from the 2010 version of the Behavioral Risk Factor Surveillance System (BRFSS) database1. The selected study on the length of time since last routine checkup was listed under the SAS Variable Name – CHECKUP1.This data set specified how long it had been since the respondent had been in for a routine checkup‚ defining
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