The Statistical Analysis of Experimental Data


Book Description

First half of book presents fundamental mathematical definitions, concepts, and facts while remaining half deals with statistics primarily as an interpretive tool. Well-written text, numerous worked examples with step-by-step presentation. Includes 116 tables.




Statistical Treatment of Experimental Data


Book Description

Even with a limited mathematics background, readers can understand what statistical methods are & how they may be used to obtain the best possible results from experimental measurements & data.




Experimental Design and Data Analysis for Biologists


Book Description

Regression, analysis of variance, correlation, graphical.




Experimental Methods


Book Description

This concise and easy to read text introduces first year students to the analysis and presentation of experimental data. Written for students taking introductory physics courses at tertiary level, Experimental Methods will be a vital resource for all students involved in experimental or laboratory work. It will be equally useful for other quantitative subjects such as chemistry, engineering and geology. Topics of fundamental importance such as keeping a laboratory notebook, analysing experimental data and report writing are often dealt with in separate texts. This book integrates these topics and provides many of the tools that students will need at first year level and beyond.




Experimental data


Book Description




Data Analysis for Experimental Design


Book Description

This engaging text shows how statistics and methods work together, demonstrating a variety of techniques for evaluating statistical results against the specifics of the methodological design. Richard Gonzalez elucidates the fundamental concepts involved in analysis of variance (ANOVA), focusing on single degree-of-freedom tests, or comparisons, wherever possible. Potential threats to making a causal inference from an experimental design are highlighted. With an emphasis on basic between-subjects and within-subjects designs, Gonzalez resists presenting the countless "exceptions to the rule" that make many statistics textbooks so unwieldy and confusing for students and beginning researchers. Ideal for graduate courses in experimental design or data analysis, the text may also be used by advanced undergraduates preparing to do senior theses. Useful pedagogical features include: Discussions of the assumptions that underlie each statistical test Sequential, step-by-step presentations of statistical procedures End-of-chapter questions and exercises Accessible writing style with scenarios and examples This book is intended for graduate students in psychology and education, practicing researchers seeking a readable refresher on analysis of experimental designs, and advanced undergraduates preparing senior theses. It serves as a text for graduate level experimental design, data analysis, and experimental methods courses taught in departments of psychology and education. It is also useful as a supplemental text for advanced undergraduate honors courses.




Cross Section and Experimental Data Analysis Using EViews


Book Description

A practical guide to selecting and applying the most appropriate model for analysis of cross section data using EViews. "This book is a reflection of the vast experience and knowledge of the author. It is a useful reference for students and practitioners dealing with cross sectional data analysis ... The strength of the book lies in its wealth of material and well structured guidelines ..." Prof. Yohanes Eko Riyanto, Nanyang Technological University, Singapore "This is superb and brilliant. Prof. Agung has skilfully transformed his best experiences into new knowledge ... creating a new way of understanding data analysis." Dr. I Putu Gede Ary Suta, The Ary Suta Center, Jakarta Basic theoretical concepts of statistics as well as sampling methods are often misinterpreted by students and less experienced researchers. This book addresses this issue by providing a hands-on practical guide to conducting data analysis using EViews combined with a variety of illustrative models (and their extensions). Models having numerically dependent variables based on a cross-section data set (such as univariate, multivariate and nonlinear models as well as non-parametric regressions) are concentrated on. It is shown that a wide variety of hypotheses can easily be tested using EViews. Cross Section and Experimental Data Analysis Using EViews: Provides step-by-step directions on how to apply EViews to cross section data analysis - from multivariate analysis and nonlinear models to non-parametric regression Presents a method to test for all possible hypotheses based on each model Proposes a new method for data analysis based on a multifactorial design model Demonstrates that statistical summaries in the form of tabulations are invaluable inputs for strategic decision making Contains 200 examples with special notes and comments based on the author’s own empirical findings as well as over 400 illustrative outputs of regressions from EViews Techniques are illustrated through practical examples from real situations Comes with supplementary material, including work-files containing selected equation and system specifications that have been applied in the book This user-friendly introduction to EViews is ideal for Advanced undergraduate and graduate students taking finance, econometrics, population, or public policy courses, as well as applied policy researchers.




Designing Experiments and Analyzing Data


Book Description

Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features further facilitate understanding: examples of published research demonstrate the applicability of each chapter’s content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and extensive sets of exercises help develop a deeper understanding of the subject. Detailed solutions for some of the exercises and realistic data sets are included on the website (DesigningExperiments.com). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.




Targeted Learning


Book Description

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.




Analysis and Presentation of Experimental Results


Book Description

This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical methods and the theory of errors. After presenting basic theoretical concepts, the book describes the methods by which the results can be presented, both numerically and graphically. The book is divided into three parts, of roughly equal length, addressing the theory, the analysis of data, and the presentation of results. Examples are given and problems are solved using the Excel, Origin, Python and R software packages. In addition, programs in all four languages are made available to readers, allowing them to use them in analyzing and presenting the results of their own experiments. Subjects are treated at a level appropriate for undergraduate students in the natural sciences, but this book should also appeal to anyone whose work involves dealing with experimental results.