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Statistical Design And Analysis Of Experiments

The scope of the book is to provide some background on statistical fundamentals that are most relevant to the biomedical researcher and to provide examples for running and interpreting various statistical functions. These researchers test ideas by generating data after manipulating some independent variable(s). They need to know principles of sampling, error, statistical hypotheses, types of data and experimental design.

Statistical design and analysis of experiments

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PSY 411 - Statistical Design and Analysis of Experiments(3 units)Prerequisites: PSY 301 , PSY 310 , PSY majors. Freshmen excluded.Focuses on logic, application, and interpretation of analysis of variance (ANOVA) models in addition to other statistical procedures. Various issues of research design and experimentation are also covered.Letter grade only (A-F). Double Numbered with: PSY 511

This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields.

Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software.

EMIS 7377 - Statistical Design and Analysis of ExperimentsCredits: 3Introduction to statistical principles in the design and analysis of industrial experiments. Completely randomized, randomized complete and incomplete block, Latin square, and Plackett-Burman screening designs. Complete and fractional experiments. Descriptive and inferential statistics. Analysis of variance models. Mean comparisons. Prerequisites: EMIS 3340 or EMIS 7370 with a science or engineering major, or permission of instructor.

For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.

The conference series Design and Analysis of Experiments (DAE) grew out of a conference that was held in 2000 at The Ohio State University. While this initial conference concentrated on the Midwest, subsequent conferences became truly international in terms of participants and expanded to North America in terms of locations. The latter have included Vancouver, BC (2002), Chicago, IL (2003), Santa Fe, NM (2005, ), Memphis, TN (2007, ) and Columbia, MO (2009, ). The purpose of the DAE conference series is to provide support and encouragement to junior researchers in the field of design and analysis of experiments and to stimulate interest in topics of practical relevance to science and industry.

Familiarizes students with the application of statistical quality problem-solving methodologies used to characterize, leverage, and reduce process variability. This course emphasizes the application of sampling methodologies, sample size determination, hypothesis testing, analysis of variance, correlation, regression, measurement systems analysis, design and analysis of saturated experimental designs, design and analysis response surface experimental designs, and statistical process control.

This course aims to provide students with a general background of various statistical analysis techniques and data mining methods that are used in transportation systems. It covers various practical analytical topics in transportation and logistics, including model estimation, data analysis, traffic forecasting, and incident prediction. A broad range of transportation related techniques are covered in statistics and data analysis skills, such as Logistic Regression, and Time Series Modeling. Popular statistical modeling software will be used to solve various practical problems.

This course provides techniques for testing hypotheses and making numerical estimates based on data collected on human subjects. The lecture content covers measurement strategies, issues of simulation fidelity, and laboratory vs. field experimentation. The laboratory content provides a series of tests of current issues in human factors and ergonomics practice from manufacturing, transportation, and healthcare. Topics will include assessing injury risk, balance and posture control, human motion analysis, muscle activity, fatigue, ergonomics for special populations such as the aging and obese, and the combined effects of mental and physical demands. Readings will be selected to put the use of various instruments and measurement systems into an ergonomics perspective. During the course of this class, we will examine the basis of data collection and analysis, and perform a series of small, complete studies designed to demonstrate different data collection/analysis techniques.

This introductory course on computer simulation covers spreadsheet simulation, discrete event simulation, system dynamics simulation and agent-based simulation with the focus on key statistical analysis of data and practice-oriented theory. Topics include generating random numbers and varieties, selecting input probability distribution, hypothesis testing for the statistical and practical significance of simulation through lab assignments, and test their gained skills in team projects inspired by real world simulation applications.

E-business draws on all the electronic activities of supply chain management. Due to the heterogeneity and the large volume of electronic data generated through e-business, advanced engineering methods are required to make optimal decisions. This course emphasizes quantitative engineering methods and statistical analysis to improve the design and efficiency of these complex networks. Methodologies include integer programming, data mining, probabilistic modeling, and multivariate analysis to assess and inform decisions about the supply chain for e-businesses. A variety of e-business applications will also be considered.

This course will start with the fundamentals of individual and group decision analysis, introduce both sequential and simultaneous-move models, for both games of complete and incomplete information. This course will then introduce advanced topics such as mechanism design, signaling, screening, repeated games, behavioral games and evolutionary games. Finally, this course will introduce some state-of-the-art game-theoretic research on supply chain management, transportation, health care, architectural design, and homeland security. Each student will work on a separate project throughout the semester, including presentations and written reports.

Both from a theoretical and practical perspective, Multiple Criteria Decision Making (MCDM) influences all aspects of engineering design, analysis and decision making. The goal of MCDM is to help a human decision maker (DM) consider several conflicting objectives simultaneously to find one or more Pareto optimal solutions that satisfy a DM's preferences. Trade-offs must be considered since no single solution individually optimizes each criterion. Theory and application will be studied. Methods can be classified as (1) No-preference methods (2) a Priori methods (DM preference information before considering alternatives (3) A posteriori methods (DM preference information after generating alternatives) and (4) Interactive methods (solution algorithms formed with DM preference information and repeated with new information at each iteration).

We propose a family of linear mixed effects models, and a split-plot view of the experimental design, that represent measurements from quantitative mass spectrometry-based proteomics. The whole plot part of the design reflects the structure of the biological variation of the experiment, such as case-control design, paired design, or time-course design. The subplot part of the design reflects the structure of the technological variation, such as fragmentation patterns, labeling strategy, and presence of multiple peptides per protein. We propose an estimation procedure that separately estimates the parameters of the subplot and the whole plot parts of the design, to maximize the flexibility of the model, increase the speed of the analysis, and facilitate the interpretation. 041b061a72


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