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ANOVA and Mixed Models : A Short Introduction Using R
ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs.Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced.The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R.From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R.The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications. FeaturesAccessible to readers with a basic background in probability and statisticsCovers fundamental concepts of experimental design and cause-effect relationshipsIntroduces classical ANOVA models, including contrasts and multiple testingProvides an example-based introduction to mixed modelsFeatures basic concepts of split-plot and incomplete block designsR code available for all stepsSupplementary website with additional resources and updates are available here.This book is primarily aimed at students, researchers, and practitioners from all areas who wish to analyze corresponding data with R.Readers will learn a broad array of models hand-in-hand with R, including the applications of some of the most important add-on packages.
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What do gymnasts or track and field athletes wear under their competition outfits?
Gymnasts and track and field athletes typically wear compression shorts or briefs under their competition outfits. These undergarments provide support and coverage while allowing for freedom of movement. They also help to prevent chafing and offer a layer of protection during high-impact movements. Additionally, some athletes may choose to wear specialized performance underwear designed to wick away moisture and provide additional support.
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What is the interaction effect in a mixed ANOVA?
The interaction effect in a mixed ANOVA refers to the combined effect of two or more independent variables on the dependent variable. It indicates whether the effect of one independent variable on the dependent variable is influenced by the levels of another independent variable. In other words, it shows whether the effect of one factor depends on the level of another factor. The presence of an interaction effect suggests that the relationship between the independent variables and the dependent variable is not simply additive.
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Is the Levene test for homogeneity of variances the same as one-way ANOVA?
No, the Levene test for homogeneity of variances is a separate statistical test used to assess whether the variances of the groups being compared in an ANOVA are equal. On the other hand, one-way ANOVA is a hypothesis test used to determine whether there are statistically significant differences between the means of three or more independent groups. The Levene test is often conducted before performing an ANOVA to ensure that the assumption of homogeneity of variances is met.
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How to perform an alpha correction in an ANOVA with Bonferroni post-hoc test?
To perform an alpha correction in an ANOVA with Bonferroni post-hoc test, you first need to determine the desired alpha level for the overall analysis. Then, divide this alpha level by the number of planned comparisons in the post-hoc test (e.g., number of groups being compared). This adjusted alpha level will be used to determine statistical significance for each individual comparison. By using the Bonferroni correction, you reduce the likelihood of making a Type I error when conducting multiple comparisons.
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ANOVA and Mixed Models : A Short Introduction Using R
ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs.Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced.The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R.From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R.The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications. FeaturesAccessible to readers with a basic background in probability and statisticsCovers fundamental concepts of experimental design and cause-effect relationshipsIntroduces classical ANOVA models, including contrasts and multiple testingProvides an example-based introduction to mixed modelsFeatures basic concepts of split-plot and incomplete block designsR code available for all stepsSupplementary website with additional resources and updates are available here.This book is primarily aimed at students, researchers, and practitioners from all areas who wish to analyze corresponding data with R.Readers will learn a broad array of models hand-in-hand with R, including the applications of some of the most important add-on packages.
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An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA
An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA Complete reference for applied statisticians and data analysts that uniquely covers the new statistical methodologies that enable deeper data analysis An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA provides readers with powerful new statistical methodologies that enable deeper data analysis.The book offers applied statisticians an introduction to the latest topics in nonparametrics.The worked examples with supporting R code provide analysts the tools they need to apply these methods to their own problems.Co-authored by an internationally recognised expert in the field and an early career researcher with broad skills including data analysis and R programming, the book discusses key topics such as: NP ANOVA methodologyCochran-Mantel-Haenszel (CMH) methodology and designLatin squares and balanced incomplete block designsParametric ANOVA F tests for continuous dataNonparametric rank tests (the Kruskal-Wallis and Friedman tests)CMH MS tests for the nonparametric analysis of categorical response data Applied statisticians and data analysts, as well as students and professors in data analysis, can use this book to gain a complete understanding of the modern statistical methodologies that are allowing for deeper data analysis.
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Design and Analysis in Educational Research Using jamovi : ANOVA Designs
Design and Analysis in Educational Research Using jamovi is an integrated approach to learning about research design alongside statistical analysis concepts.Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research. Based on their successful SPSS version of the book, the authors focus on using jamovi in this version due to its accessibility as open source software, and ease of use.The book teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA. This textbook is tailor-made for first-level doctoral courses in research design and analysis.It will also be of interest to graduate students in education and educational research.The book includes Support Material with downloadable data sets, and new case study material from the authors for teaching on race, racism, and Black Lives Matter, available at www.routledge.com/9780367723088.
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The Detailed Anova Sous Vide Cookbook : 600 Tasty and Unique Recipes for Smart People on A Budget
This book empowers you to become a true sous vide master chef and wow your friends, family and neighbors with juicy, tender, perfectly cooked meats and veggies every time. This comprehensive resource ensures you'll be equipped to take on any sous vide challenge and enjoy elevated gourmet cuisine every night of the week. The Detailed Anova Sous Vide Cookbook is your all-in-one resource for enjoying the very best sous vide meals you've ever tasted!The Detailed Anova Sous Vide Cookbook contains:600 Easy to Follow Recipes using ingredients that are easily found at your local grocery store and that are ideal for cooking in your Anova Sous Vide Precision Cooker - each with complete nutritional information.Delicious Sous Vide Meals For Every Occasion conveniently indexed and organized into chapters including Beef, Lamb, Chicken, Pork, Seafood, Vegetable, and Dessert recipes.A Complete Overview of sous vide cooking with your Anova Sous Vide, detailing the essential information you need to know to earn your reputation as the local master chef.Handy Charts and Indexes including temperature and conversion tables so all the information you need is at your fingertips.Get a copy of this The Detailed Anova Sous Vide Cookbook and makes tasty food easy for you and Your Whole Family!
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How to conduct an alpha correction in an ANOVA with Bonferroni post-hoc test?
To conduct an alpha correction in an ANOVA with Bonferroni post-hoc test, you first need to determine the overall significance level you want to use for the entire family of comparisons. Divide this significance level (usually 0.05) by the number of planned comparisons to get the adjusted alpha level for each individual comparison. Then, compare the p-values from the post-hoc tests to the adjusted alpha level to determine statistical significance. This correction helps reduce the likelihood of making a Type I error when conducting multiple comparisons.
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Is the Levene test for homogeneity of variances the same as the one-way ANOVA?
No, the Levene test for homogeneity of variances is a separate statistical test from the one-way ANOVA. The Levene test is used to determine if the variances of the groups being compared in an ANOVA are equal. It tests the null hypothesis that the variances are equal across all groups. On the other hand, the one-way ANOVA is used to test the null hypothesis that the means of the groups are equal. While both tests are related to comparing groups, they are testing different aspects of the data.
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Should Let's Dance be a dance competition without acrobatics?
Let's Dance should be a dance competition without acrobatics. Acrobatics can overshadow the true art of dance and may not be accessible to all participants. Focusing solely on dance allows for a fair and equal platform for all dancers to showcase their skills and creativity. It also promotes the preservation and celebration of various dance styles and traditions.
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What do I need to calculate if my two-way repeated measures ANOVA is not normally distributed?
If your two-way repeated measures ANOVA is not normally distributed, you may need to calculate a non-parametric alternative test, such as the Friedman test. This test does not assume normality and is appropriate for analyzing repeated measures data when the assumptions of ANOVA are not met. Additionally, you may need to consider transforming your data or using robust statistical methods to account for the violation of normality assumption. It is important to assess the impact of the non-normality on your results and interpret them accordingly.
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