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Author: Thomas W. MacFarland Publisher: Springer Science & Business Media ISBN: 1461421330 Category : Social Science Languages : en Pages : 146
Book Description
In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA.
Author: Thomas W. MacFarland Publisher: Springer Science & Business Media ISBN: 1461421330 Category : Social Science Languages : en Pages : 146
Book Description
In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA.
Author: Daniel Navarro Publisher: Lulu.com ISBN: 1326189727 Category : Computers Languages : en Pages : 617
Book Description
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Author: Amanda Ross Publisher: Springer ISBN: 9463510869 Category : Education Languages : en Pages : 223
Book Description
This book focuses on extraction of pertinent information from statistical test outputs, in order to write result sections and/or accompanying tables and/or figures. The book is divided into two encompassing sections: Part I – Basic Statistical Tests and Part II – Advanced Statistical Tests. Part I includes 9 basic statistical tests, and Part II includes 7 advanced statistical tests. Each chapter provides the name of a basic or advanced statistical test, a brief description, examples of when to use each, a sample scenario, and a sample results section write-up. Depending on the test and need, most chapters provide a table and/or figure to accompany the write-up. The purpose of the book is to provide researchers with a reference manual for writing results sections and tables/figures in scholarly works. The authors fill a gap in research support manuals by focusing on sample write-ups and tables/figures for given statistical tests. The book assists researchers by eliminating the need to comb through numerous publications to determine necessary information to report, as well as correct APA format to use, at the close of analyses.
Author: Michael H. Herzog Publisher: Springer ISBN: 3030034992 Category : Science Languages : en Pages : 146
Book Description
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
Author: Henry Scheffé Publisher: John Wiley & Sons ISBN: 9780471345053 Category : Mathematics Languages : en Pages : 500
Book Description
Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
Author: Andrew Gelman Publisher: Cambridge University Press ISBN: 9780521686891 Category : Mathematics Languages : en Pages : 654
Book Description
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Author: George Casella Publisher: Springer Science & Business Media ISBN: 0387759646 Category : Mathematics Languages : en Pages : 325
Book Description
Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. In this book the basic theoretical underpinnings are covered. It describes the principles that drive good designs and good statistics. Design played a key role in agricultural statistics and set down principles of good practice, principles that still apply today. Statistical design is all about understanding where the variance comes from, and making sure that is where the replication is. Indeed, it is probably correct to say that these principles are even more important today.
Author: Thomas Baguley Publisher: Bloomsbury Publishing ISBN: 0230363555 Category : Psychology Languages : en Pages : 864
Book Description
Ideal for experienced students and researchers in the social sciences who wish to refresh or extend their understanding of statistics, and to apply advanced statistical procedures using SPSS or R. Key theory is reviewed and illustrated with examples of how to apply these concepts using real data.
Author: Ronald Christensen Publisher: CRC Press ISBN: 9780412062919 Category : Mathematics Languages : en Pages : 608
Book Description
This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.
Author: Thomas W. MacFarland Publisher: Springer Science & Business Media ISBN: 1461421349 Category : Social Science Languages : en Pages : 145
Book Description
In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories. When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA.
Author: Thomas W. MacFarland
Author: Thomas W. MacFarland
Author: Daniel Navarro
Author: Amanda Ross
Author: Michael H. Herzog
Author: Henry Scheffé
Author: Andrew Gelman
Author: George Casella
Author: Thomas Baguley
Author: Ronald Christensen
Author: Thomas W. MacFarland