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• The Iterative Modeling Process and Inverse Problems
• Statistical Hypothesis Testing in Inverse Problems
• Mass Balance and Mass Transport
• Heat Conduction
• Force/Moments Balance (Structural Modeling)
• Wave Equations
• Size-Structured Population Models
• Appendices: An Introduction to Fourier Techniques, Fourier Series, Fourier Transforms, Review of Vector Calculus.
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Through several case study problems from industrial and scientific research laboratory applications, "Mathematical and Experimental Modeling of Physical and Biological Processes" provides students with a fundamental understanding of how mathematics is applied to problems in science and engineering. For each case study problem, the authors discuss why a model is needed and what goals can be achieved with the model. Exploring what mathematics can reveal about applications, the book focuses on the design of appropriate experiments to validate the development of mathematical models. It guides students through the modeling process, from empirical observations and formalization of properties to model analysis and interpretation of results. The authors also describe the hardware and software tools used to design the experiments so faculty/students can duplicate them. Integrating real-world applications into the traditional mathematics curriculum, this textbook deals with the formulation and analysis of mathematical models in science and engineering. It gives students an appreciation of the use of mathematics and encourages them to further study the applied topics.
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• Basic Descriptions and Properties
• Linear Physical Systems
• Probability Fundamentals
• Statistical Principles
• Stationary Random Processes
• Single-Input/Output Relationships
• Multiple-Input/Output Relationships
• Statistical Errors in Basic Estimates
• Statistical Errors in Advanced Estimates
• Data Acquisition and Processing
• Digital Data Analysis
• Nonstationary Data Analysis
• The Hilbert Transform
• Hilbert Transforms for Correlation Functions.
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This book covers techniques for the analysis and measurement of stationery and nonstationery random data passing through physical systems.
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A revised and expanded edition of this reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems.
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• Basic descriptions and properties
• linear physical systems
• probability fundamentals
• statistical principles
• stationary random processes
• single-input/output relationships
• multiple-input/output relationships
• statistical errors in basic estimates
• statistical estimates in advanced estimates
• data acquisition and processing
• digital data analysis
• nonstationary data analysis
• the Hilbert transform. Appendixes: statistical tables
• definitions for random data analysis.
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The classic reference on the theory and application of random data analysis-now expanded and revised. This eagerly awaited new edition of the bestselling random data analysis book continues to provide first-rate, practical tools for scientists and engineers who investigate dynamic data as well as those who use statistical methods to solve engineering problems. It is fully updated, covering new procedures developed since 1986 and extending the discussion to a remarkably broad range of applied fields, from aerospace and automotive industries to biomedical research. Comprehensive and self-contained, this new edition also greatly expands coverage of the theory, including derivations of key relationships in probability and random process theory not usually found in books of this kind. Special features of Random Data: Analysis and Measurement Procedures, Third Edition include: Basic probability functions for level crossings and peak values of random data Complete derivations of both old and new practical formulas for statistical error analysis of computed estimates The latest methods for data acquisition and processing as well as nonstationary data analysis Additional techniques on digital data analysis procedures New material on the analysis of multiple-input/multiple-output linear systems Numerous new examples and problem sets Hundreds of updated illustrations and references.
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• Preface.- 1 Introduction to the Geometric Theory of ODEs with Applications to Chemical Processes, Dietrich Flockerzi.- 2 Mathematical Modeling and Analysis of Nonlinear Time-Invariant RLC Circuits, Timo Reis.- 3 Interacting with Networks of Mobile Agents, Magnus Egerstedt, Jean-Pierre de la Croix, Hiroaki Kawashima, and Peter Kingston.- 4 Combinatorial optimization: the interplay of graph theory, linear and integer programming illustrated on network flow, Annegret K. Wagler.- 5 Stoichiometric and Constraint-based Analysis of Biochemical Reaction Networks, Steffen Klamt, Oliver Hadicke and Axel von Kamp.- 6 A Petri Net based Framework for Biomodel Engineering, Mary Ann Blatke, Christian Rohr, Monika Heiner and Wolfgang Marwan.- 7 Hybrid Modeling for Systems Biology: Theory and Practice, Moritz von Stosch, Nuno Carinhas and Rui Oliveira.
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This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines. The book originates from the interdisciplinary summer school "Large Scale Networks in Engineering and Life Sciences" hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of interest to mathematicians, engineers, physicists, biologists, chemists, and anyone involved in the network sciences. In particular, due to their introductory nature the chapters can serve individually or as a whole as the basis of graduate courses and seminars, future summer schools, or as reference material for practitioners in the network sciences.
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• Getting Started.Exploring Excel.Excel's Graphics.Quick-Start Math.Differentiation and Integration.Excel's Engineering Functions.Differential Equations.Analysis ToolPak.Excel in the Lab.The following chapters are on the CD only:Complex Math.Analysis ToolPak: Fourier Analysis.Analysis ToolPak: FFT Applications.
• Appendix 1: Workbooks on the Disk.
• Appendix 2: Internet Links.
• Appendix 3: Trendline Equations.
• Appendix 4: Goal Seek, Solver, Lookup Wizard.
• Appendix 5: PivotTable, Internet Assistant Wizard.Bibliography.Index.
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Using an informal, conversational style, this "how to" book guides beginning students from spreadsheet basics through the robust engineering and scientific applications of EXCEL, including using EXCEL in the lab. Students learn how to compose structured, efficient, documented workbooks with data entry cells, summary results and statistics cells, and commented cells. Throughout the book, they'll find innovative techniques for composing spreadsheets, solving problems, analyzing data, and presenting results that will help them in their courses and professional careers. End-of-Chapter problems not only show how to use EXCEL, they also relate directly to topics in engineering and the sciences. Plus, a CD, which is packaged with the text, contains sample workbooks, links to online EXCEL resources, and text updates via the book's web site.
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This book will be an important resource for both new graduates and mid-career scientists, engineers, and technicians. Through taking stock of existing or desired skills and goals, it provides both general advice and concrete examples to help assess a current job situation or prospect, and to effectively pursue and attain new ones. Many examples of properly adapted resumes and interview techniques, as well as plenty of practical advice about adaptation to new workplace cultural paradigms, such as team-based management, make this book an invaluable reference for the professional scientist in today's volatile job market.
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• Introduction. Financing the Corporate Venture. Financial Statements. Estimation of Capital Requirements. Estimation of Operating Expenses. Time Value of Money. Depreciation, Depletion, Amortization, and Taxes. Cash Flow Concept. Estimate of Profitability. Sensitivity and Uncertainty Analysis. Feasibility Analysis. Choice Between Alternatives and Replacement. The Economic Balance. Concluding Comments. Index.
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This reference outlines the fundamental concepts and strategies for economic assessments for informed management decisions in industry. This book illustrates how to prepare capital cost and operating expense estimates, profitability analyses, and feasibility studies, and execute sensitivity and uncertainty assessments. From financial reports to opportunity costs and engineering trade-offs, "Process Engineering Economics" considers a wide range of alternatives for profitable investing and for projecting outcomes in various chemical and engineering fields and monitors costs, finances, and economic limitations at every stage of chemical project design, preparation, and evaluation.
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• 1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability.
• 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence.
• 3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution.
• 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots.
• 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination.
• 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation.
• 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population.
• 8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean. The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing.
• 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances.
• 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA.
• 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij =
• 1. Two-Factor ANOVA with Kij >
• 1. Three-Factor ANOVA
• 11. 4 2p Factorial Experiments.
• 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter s
• s1. Inferences Concerning Y*x* and the Prediction of Future Y Values. Correlation.
• 13. NONLINEAR AND MULTIPLE REGRESSION. Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression.
• 14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables
• 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA.
• 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling.
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Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th Edition. Always a market favorite, this calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces. Jay Devore, an award-winning professor and internationally recognized author and statistician, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations. Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate book, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th Edition also includes graphics and screen shots from SAS(R), MINITAB(R), and Java(TM) Applets to give you a solid perspective of statistics in action.
(source: Nielsen Book Data)