"Applying ANOVA and Nonparametric tests" simulation‚ I realize there were a few things to take into consideration when analyzing a problem. This particular exercise wanted to know the differences and causes of the variation. In order to resolved the solution‚ an individual or whoever is conducting the analysis will need to know what type of test to used‚ decide if the null hypothesis should be rejected or not‚ and make recommendation based on the collected data. Due to two parameters‚ ANOVA and the
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Applying ANOVA and Nonparametric test In the simulation‚ I selected the Kruskal-Wallis test which is used when it is difficult to meet all of the assumptions of ANOVA. The Kruskal-Wallis test is a nonparametric alternative to one way ANOVA. This test is used to compare three or more samples‚ to test the null hypothesis that the different samples in the comparison drawn from the same distribution or from distributions with the same median. Interpretation of the Kruskal-Wallis test is basically similar
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Nonparametric Tests Basic Concepts • Sampling Distribution • Central Limit Theorem • Parametric Tests • Non Parametric Tests • When to use Nonparametric Tests? • Important Non Parametric Tests and their Parametric Alternatives • Advantages and Disadvantages of Nonparametric Tests. Useful Tests • Test of Normality. • Chi Squared Tests • One-Sample Runs Test • Wilcoxon Signed-Rank Test • Mann-Whitney Test • Kruskal-Wallis Test • Spearman Rank Correlation Test Sampling
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Nonparametric Test Education is even more important than ever today for anyone interested in entering the world of employment with either large international corporations‚ or even local vendors serving the community within the area where one lives. In an ongoing effort by our research team to determine if the difference in the wages from our sample population of men and women‚ who have various levels of education‚ does in fact make the difference. We are looking to use an additional test to discover
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26 3.04 2.8 3.75 3.64 3.65 3.18 3.44 3.51 2.81 3.64 2.85 3.56 2.92 3.35 3.46 3.59 3.65 2.97 3.21 3.65 2.94 3.53 3.65 3.61 3.7 2.91 3.77 3.79 3.59 3.38 3.57 2.97 3.44 3.48 2.99 3.73 2.91 3.78 3.13 3.14 SUMMARY Groups Unemployed Part-time Full-time ANOVA Source of Variation Treatment Error Total Null hypothesis: Alternate hypothesis: Significance level: p-Value Decision: Count 25 45 130 0 0 Sum 82.110 152.050 450.130 0.000 0.000 Average 3.284 3.379 3.463 Variance 0.110 0.085 0.091 SS 0.771 18
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Applying ANOVA and Non Parametric Tests a. What are three lessons you learned relative ANOVA and Nonparametric tests? While doing the simulation; the three lessons learned are as follows: Monitor – the situation Measure – provide measurements‚ accumulate data Improve – provide solutions for improvement. b. As a result of using this simulation‚ what concepts and analytic tools will you be able to use in your workplace (i.e.‚ how do you expect to apply what you learned)? As a result of using
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Analysis of Variance (ANOVA) Indian Institute of Public Health Delhi MSc CR 2013-15 Outline of the session • Need for Analysis of Variance • Concept behind one way ANOVA • Example • Non-parametric alternative When dependent variable is continuous Type of Dependent variable Type of Independent variable Number of Groups Continuous Categorical More than two Non-parametric (Wilcoxon sign rank) Paired t – test Not normal Non-parametric (Wilcoxon sign
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Variance (ANOVA) Dr. H. Johnson ANOVA • Analysis of variance (ANOVA) is a powerful hypothesis testing procedure that extends the capability of t-tests beyond just two samples. • Many types of ANOVAs‚ today we will learn about a oneway independent-measures ANOVA • Later we’ll learn one-way repeated-measures ANOVA . • We’ll also learn two-factor ANOVA after that. • These ANOVAs are by no means all of them! There are a LOT more types! One-Way ANOVA • The independent measures ANOVA is used
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Chapter 15: Introduction to the Design of Experimental and Observational Studies The Models in Analysis of Variance(ANOVA) and in Regression are different. In regression model‚ all the response and predictors are continuous (quantitative) variables. However‚ in ANOVA model‚ the response is continuous but the predictors are categorical (qualitative) variables. There are some concepts here. 1. Factor and factor level. A factor is a predictor (explanatory or independent) variable. A factor level is
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Iqra University‚ Main Campus Course: Statistical Inferences Faculty: Iftikhar Mubbashir Date: December 5‚ 2013 Fall 2013 Statistics-Walpole Chapter-12 One way Classification • • • • • • Random samples of size n are selected from each of k populations. The k populations are independent and normally distributed with means µ 1 ‚ µ 2 ‚K ‚ µ k and common variance σ 2 . We wish to derive appropriate methods for testing the hypothesis:
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