...Applying ANOVA and Nonparametric Tests Simulation This week’s assignment was to take a simulation called Applying ANOVA and Nonparametric Tests. After carefully reviewing the simulation it became easier to answer the questions for the assignment. Researchers sometimes have difficult decisions to make. Applying the analysis of variance (ANOVA) helps businesses to recognize the challenges and opportunities of making a business decision. ANOVA testing is a statistical tool that test each population calculated with a normal distribution (University of Phoenix, 2011). The benefit of this test is it can narrow down the errors of an incorrect test method as long as there is statistical proof (University of Phoenix, 2011). On the other hand, other tests are required because sometimes there are inaccurate assumptions that come with the testing process and businesses than acquire the nonparametric test known as the Kruskal-Wallis test for further analysis (University of Phoenix, 2011). The three lessons learned related to the ANOVA and Nonparametric tests include how businesses can learn how to better monitor, measure and improve their business processes (University of Phoenix, 2011). A successful business is faced with many challenges daily. The goal is to provide quality products and excellent services to their customer’s, employees and shareholders. After reviewing the simulation, some concepts and analytic tools came to mine, which this would...
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...Applying ANOVA and Nonparametric Tests Simulation As the Quality Assurance Manager for Praxidike Systems, it is my job to make sure delivery is on time and that the clients are satisfied. First I had to decide which type of test to use. In order to be able to use ANOVA you have to make three major assumptions: 1. Errors are random and independent of each other 2. Each population has normal distribution 3. All populations have the same variance In order to check whether or not the population has a normal distribution, you need to use the chi-square test for goodness of fit. The hypotheses in this case are: 1. H0: The population has a normal distribution. 2. HA: The population does not have a normal distribution. The outcome was that the test statistic lies outside the acceptance area and you should reject the null hypothesis. As a result, you cannot presume that the population has a normal distribution; you should use the nonparametric Kruskal-Wallis test. The second objective I learned was that you cannot always use the blocking technique. Blocking allows you to see a treatment effect with a smaller sample. It is difficult to set up blocks and it is necessary to determine if creating the block was worth the effort. When setting up a block, you need to match the variable with two or more factors. This may not always be an option. To find out if the block is optimal, you can calculate the relative efficiency. In the case of this simulation, the block design works...
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...Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation RES 342 William Modey Applying Analysis of Variance (ANOVA) and Nonparametric Tests Simulation ANOVA and Non Parametric tests can help in business endeavors wherever there is two or more variables or hypothesis. The ANOVA and Non Parametric Tests Simulation showed the various ways to do hypothesis testing with two or more hypothesis. Being able to do the various types of testing that come along with ANOVA and Non Parametric data sets is key to making the right decision when having two or more choices. The three lessons that I have learned after doing the ANOVA and Non Parametric Tests Simulation were to thoroughly analyze the presented problem before attempting to make a decision, enlist the help of others when making a decision or choosing a course of action, and to continually improve on decision making skills based on learning from past mistakes made. As a result of using this simulation the concepts and analytic tools that I would be able to use in my workplace are that I am now able to approach a decision making scenario with appropriate knowledge and testing procedures to help make the best decision. The skills that I learned in the simulation, such as the different hypothesis testing procedures, could be key to helping me improve my managerial skills. Based on my passed experiences and current knowledge, I would recommend that the key decision maker take his or her time when making...
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...Applying ANOVA and Nonparametric Tests Berdie Thompson RES/342 October 17th, 2011 Olivia Scott Applying ANOVA and Nonparametric Tests In the simulation regarding applying ANOVA and nonparametric tests, the problem being addressed is the farmer, Samuel, and his corn crop not yielding a good crop to harvest. Samuel needed to run tests to determine the reason why his neighbor’s crop grew and his did not. There are different factors that can contribute to Samuel not yielding a good corn crop such as: variety of corn, sunlight, moisture, soil type, and so on. Samuel needed to determine which test was appropriate to perform that would relay accurate results. In performing the ANOVA test, there are three important lessons that one must know. ANOVA always assumes that each population being studied has a normal distribution. The second lesson is that errors are random and independent of each other. The third lesson is that all populations have the same variance. In reviewing this simulation, it was interesting to see how these tools and concepts are intertwined with the everyday business world aspect. This simulation uses real-life situations and applies a statistical method to solving the problem. Personally, I understand things better when a real-life issue is incorporated into the problem. A tool that I learned about and plan on utilizing is called the Kruskal-Wallis test. “The Kruskal-Wallis test is used when it is difficult to meet all of the assumptions of ANOVA” (University...
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...Nonparametric Hypothesis Testing RES/342 Nonparametric Hypothesis Testing During the course of the last three weeks, the team explored the hypothesis testing segment of statistics research. The first part of this assignment was the one sample hypothesis testing. The second was the two or more sample hypothesis testing, and finally in this third week, we will look at nonparametric hypothesis testing. This week’s project is a continuation of the previous projects and entails to build on the identical research question that we will frame a research hypothesis from the same provided data sets (Wage and Wage Earners) using ratio or interval numerical data; however, this week we will use a nonparametric hypothesis test to find our answer. In the next following paragraphs, the team will clearly affirm a hypothesis statement that will provide the base for our survey, perform a five-step hypothesis test on information concerning our choice and apply the concepts of nonparametric testing learned in this course, and describe how the results of our findings answer our research question. Finally, we will conclude this study with a brief summary that will examine the main points, the purpose, and conclusions of this final third week’s study on nonparametric testing. Perform the five-step hypothesis test on the data Nonparametric tests are statistical tests that analyze data that does not require assumptions about the distribution of shape of the population from which...
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...|Applying Analysis of Variance | |[Anova test simulation] | |Rochelle Kuebler | |[September 23, 2011] | Praxidike is a software company, which has concern defining why their assignments are not done on time. The set-up starts by handing out two nonparametric analysis methods that include ANOVA and Kruskal-Wallis. Nonparametric testing procedures need definite requests to make use of efficiently. The key norms of ANOVA testing consist of the following: the population consumes a standard distribution, mistakes are independent, and population consumes the same variance. The Kruskal-Wallis test, instead, does not involve the hypothesis of a common distribution and the facts need to be on an ordinal measure. This is characteristically a superior choice if the expectations of ANOVA will not be met. This setup runs three examples of how certain testing systems can be practical to real-world circumstances. The first part of the situation is to relate the Kruskal-Wallis test because the expectations of ANOVA may not be seen. After studying the facts, it was obvious that the level of capability dealing with the software...
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...Applying ANOVA and Nonparametric Tests Simulation In this week’s simulation, I chose the Kruskal-Wallis test. The three lessons learned relative to ANOVA and Nonparametric Tests were the errors are random and independent of each other, each population has a normal distribution, and all the populations have the same variance. From the lessons learned through the ANOVA and Nonparametric Tests Simulation, I will be able to apply the concepts and analytical tools learned at my workplace by applying ANOVA and various nonparametric tests to analyze the results of data for more efficient and effective operations within my organization. Some of the suggestions that I made in the simulation for month one were to conduct a Kruskal-Wallis Test, reject the null hypothesis, and provide training for increase competency, which will help increase productivity of the software engineers of Praxidike Systems. For month two, I selected the type of project and scope changes as factors for analysis. The correlation matrix showed that the factor “type of project” has a strong positive correlation with the productivity of software engineers. The factor “scope changes,” on the other hand, has a correlation coefficient of 0.6. It was difficult to make any definite conclusions from this number as it was more than 0.5, but less than 0.75. Thus, further investigation of the factor is appropriate. I also made suggestions to set competency levels for a project depending on the skill requirements...
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...ANOVA and Nonparametric Simulation For this assignment students are tasked with completing an online simulation and applying statistical research. To complete this paper we as students need to answer three basic questions. What are three lessons you learned relative to ANOVA and nonparametric tests? 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)? Based on your experience, what additional information would you recommend to the key decision maker in the simulation to solve the challenge given? For the first question relating to lessons learned; ANOVA is a test to compare the means of several populations. ANOVA doesn’t answer which population might have a larger or smaller mean: it only answers whether all the means are equal. This is done by evaluating the variances within the groups and between the groups being compared (Doane & Seward, 2007). ANOVA can be used to evaluate if different factors have a different effect on a variable. The factors are categorical variables with different levels. For instance, the context of the simulation is a software development company that wants to know if factors like project difficulty, experience, or requirement changes affect the productivity of the software engineers. Productivity is measured by the average number of lines of code written per day by a programmer. An ANOVA test can shed light as to whether productivity...
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...Variance and Nonparametric Tests Paper Andy Martinez Res/342 February 1, 2012 Victor Ornelas Most recent concepts are ANOVA and nonparametric tests. ANOVA, also known as analysis of variance is a concept that allows you to “compare more than two means simultaneously and how to trace sources of variation to potential explanatory factors”. One of the biggest take away from this concept is that the ANOVA tests can take on many factors or “treatments. This can be very beneficial when dealing with many factors. Although this does not mean that it is the norm to have many factors. Most researchers focus on a limited amount of factors. Another big lesson is with nonparametric tests. Working for a company that requires that we learn how comfortable our users are with their technology it is very important to use ordinal data for informative decision making. In our data sets there is a large majority of information that does not come in with a normal distribution. This is where it comes into play that a parametric test can aid due to the ability to examine information without normal distribution. One of the largest lessons in terms of these concepts is the data size that it takes to utilize these tests. Unfortunately as with any questionnaire my department usually gets only a small amount back. When working with parametric testing a small sample size would only go to hurt the result of the testing. When working with these tests it is possible to extrapolate the data...
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...Applying ANOVA and Nonparametric Tests Simulation Many organizations use various tools to ensure quality assurance and management for their business. The challenge for them is to ensure that they provide the best quality of service to their clients in a time effective manner. As such, having a diversity of tool options in place helps the organization identify daily challenges and increase overall effectiveness practices in their decision making processes. Implicitly, identifying the problems is the first key component towards making a sound decision. Once the problems are identified organizations can use tests such as ANOVA, nonparametric test and Kruskal-Wallis test for operational research methods and total quality management. These methods will allow researchers to analyze significant data that will subsequently result in implementation of the found solutions. Accordingly, the Praxidike Systems Corporation has identified a problem with their turn-around time in delivering products to their clients. With that, using the ANOVA, nonparametric and Kruskal- Wallis test has taught me that utilizing these tools can assist in analyzing information in order to make the best possible decision for the organization. Furthermore, using these tools help make the process easier to control by breaking down the data into various groups in order to manage the information. When using these tools, it is easy to apply what was reviewed to everyday life especially...
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...make these determinations and allow us to generalize the results to a larger population. We provide background about parametric and nonparametric statistics and then show basic inferential statistics that examine associations among variables and tests of differences between groups. Parametric and Nonparametric Statistics In the world of statistics, distinctions are made in the types of analyses that can be used by the evaluator based on distribution assumptions and the levels of measurement data. For example, parametric statistics are based on the assumption of normal distribution and randomized sampling that results in interval or ratio data. The statistical tests usually determine significance of difference or relationships. These parametric statistical tests commonly include t-tests, Pearson product-moment correlations, and analyses of variance. Nonparametric statistics are known as distribution-free tests because they are not based on the assumptions of the normal probability curve. Nonparametric statistics do not specify conditions about parameters of the population but assume randomization and are usually applied to nominal and ordinal data. Several nonparametric tests do exist for interval data, however, when the sample size is small and the assumption of normal distribution would be violated. The most common forms of nonparametric...
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...GLOSSARY −−2 log likelihood (ratio) test: Used in logistic regression, it is a form of chi-square test which compares the goodness of-fit of two models where one model is a part of (i.e. nested or a subset of) the other model. The chi-square is the difference in the –2 log likelihood values for the two models. A priori test: A test of the difference between two groups of scores when this comparison has been planned ignorant of the actual data. This contrasts with a post hoc test which is carried out after the data have been collected and which has no particularly strong expectations about the outcome. Adjusted mean: A mean score when the influence of one or more covariates has been removed especially in analysis of covariance. Alpha level: The level of risk that the researcher is prepared to mistakenly accept the hypothesis on the basis of the available data. Typically this is set at a maximum of 5% or .05 and is, of course, otherwise referred to as the level of significance. Analysis of covariance (ANCOVA): A variant of the analysis of variance (ANOVA) in which scores on the dependent variable are adjusted to take into account (control) a covariate(s). For example, differences between conditions of an experiment at pre-test can be controlled for. Analysis of variance (ANOVA): An extensive group of tests of significance which compare means on a dependent variable. There may be one or more independent (grouping) variables or factors. ANOVA is essential in the analysis of most...
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...BEDAN. ADMISSION REF-27086/2013 LECTURER; Dr. Lilian Otieno, Resident Lecturer I am tasked to distinguish between parametric and non-parametric statistics and explain when to use each method in analysis of data. I shall first seek to define what parametric and non-parametric statistics mean and then compare and contrast them in the analysis of data. Parametric statistics is a branch of statistics that assumes that the data has come from a type of probability distribution and makes inferences about the parameters of the distribution. Most well-known elementary statistical methods are parametric. (According to Wikipedia, the online dictionary). In statistical analysis, parametric significance tests are only valid if certain assumptions are met. If they are not, nonparametric tests can be used. A parameter is a measure of an entire population, such as the mean height of every man in London. In statistical 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 considered robust. On the other hand, parametric formulae are often simpler to...
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...The terms "statistical analysis" and "data analysis" can be said to mean the same thing -- the study of how we describe, combine, and make inferences based on numbers. A lot of people are scared of numbers (quantiphobia), but data analysis with statistics has got less to do with numbers, and more to do with rules for arranging them. It even lets you create some of those rules yourself, so instead of looking at it like a lot of memorization, it's best to see it as an extension of the research mentality, something researchers do anyway (i.e., play with or crunch numbers). Once you realize that YOU have complete and total power over how you want to arrange numbers, your fear of them will disappear. It helps, of course, if you know some basic algebra and arithmetic, at a level where you might be comfortable solving the following equation There are three (3) general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics. 1. Descriptive statistics fall into one of two categories: measures of central tendency (mean, median, and mode) or measures of dispersion (standard deviation and variance). Their purpose is to explore hunches that may have come up during the course of the research process, but most people compute them to look at the normality of their numbers. Examples include descriptive analysis of sex, age, race, social class, and so forth. 2. Relationalstatistics fall into one of three categories: univariate,...
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...|[pic] |Course Syllabus | | |College of Natural Sciences | | |MTH/233 Version 2 | | |Statistics | Copyright © 2010, 2006 by University of Phoenix. All rights reserved. Course Description This course surveys descriptive and inferential statistics with an emphasis on practical applications of statistical analysis. The principles of collecting, analyzing, and interpreting data are covered. It examines the role of statistical analysis, statistical terminology, the appropriate use of statistical techniques and interpretation of statistical findings through applications and functions of statistical methods. Policies Faculty and students/learners will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to...
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