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Statistics for Environmental Engineers 2nd edition [Kietas viršelis]

(Tufts University, Medford, Massachusetts, USA), (University of Wisconsin, Madison, USA)
  • Formatas: Hardback, 502 pages, aukštis x plotis: 254x178 mm, weight: 1043 g, 193 Illustrations, black and white
  • Išleidimo metai: 29-Jan-2002
  • Leidėjas: CRC Press Inc
  • ISBN-10: 1566705924
  • ISBN-13: 9781566705929
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 502 pages, aukštis x plotis: 254x178 mm, weight: 1043 g, 193 Illustrations, black and white
  • Išleidimo metai: 29-Jan-2002
  • Leidėjas: CRC Press Inc
  • ISBN-10: 1566705924
  • ISBN-13: 9781566705929
Kitos knygos pagal šią temą:
The 54 short chapters in this textbook apply the prevalent statistical methods to sample analyses usually performed in environmental engineering. The second edition adds 13 chapters on such techniques as weighted least squares, time series modeling, and transfer function models. Annotation (c) Book News, Inc., Portland, OR (booknews.com)

Two critical questions arise when one is confronted with a new problem that involves the collection and analysis of data. How will the use of statistics help solve this problem? Which techniques should be used? Statistics for Environmental Engineers, Second Edition helps environmental science and engineering students answer these questions when the goal is to understand and design systems for environmental protection. The second edition of this bestseller is a solutions-oriented text that encourages students to view statistics as a problem-solving tool.

Written in an easy-to-understand style, Statistics for Environmental Engineers, Second Edition consists of 54 short, "stand-alone" chapters. All chapters address a particular environmental problem or statistical technique and are written in a manner that permits each chapter to be studied independently and in any order. Chapters are organized around specific case studies, beginning with brief discussions of the appropriate methodologies, followed by analysis of the case study examples, and ending with comments on the strengths and weaknesses of the approaches.

New to this edition:

  • Thirteen new chapters dealing with topics such as experimental design, sizing experiments, tolerance and prediction intervals, time-series modeling and forecasting, transfer function models, weighted least squares, laboratory quality assurance, and specialized control charts
  • Exercises for classroom use or self-study in each chapter
  • Improved graphics
  • Revisions to all chapters

    Whether the topic is displaying data, t-tests, mechanistic model building, nonlinear least squares, confidence intervals, regression, or experimental design, the context is always familiar to environmental scientists and engineers. Case studies are drawn from censored data, detection limits, regulatory standards, treatment plant performance, sampling and measurement errors, hazardous waste, and much more. This revision of a classic text serves as an ideal textbook for students and a valuable reference for any environmental professional working with numbers.
  • Recenzijos

    "The book is well written, easy to read, and interesting, which is no small feat considering the subject matter. The authors have taken considerable steps to make this textbook user-friendly to their intended audience, environmental engineers. The authors, both recognized experts in civil and sanitary engineering, provide data and problems in each chapter that use relevant and realistic examples to teach the concepts of each chapter. [ U]seful and well written [ the book] contains exercises based on the types of real-world problems that environmental engineers face on a daily basis." - Environmental Practice, Vol. 6, No. 4, Dec. 2004



    About the first edition: "...a valuable addition to any environmental engineer's library." -Technometrics

    Environmental Problems and Statistics
    1(6)
    A Brief Review of Statistics
    7(18)
    Plotting Data
    25(16)
    Smoothing Data
    41(6)
    Seeing the Shape of a Distribution
    47(8)
    External Reference Distributions
    55(6)
    Using Transformations
    61(10)
    Estimating Percentiles
    71(6)
    Accuracy, Bias, and Precision of Measurements
    77(10)
    Precision of Calculated Values
    87(10)
    Laboratory Quality Assurance
    97(6)
    Fundamentals of Process Control Charts
    103(10)
    Specialized Control Charts
    113(6)
    Limit of Detection
    119(10)
    Analyzing Censored Data
    129(12)
    Comparing a Mean with a Standard
    141(6)
    Paired t-Test for Assessing the Average of Differences
    147(10)
    Independent t-Test for Assessing the Difference of Two Averages
    157(4)
    Assessing the Difference of Proportions
    161(8)
    Multiple Paired Comparisons of k Averages
    169(6)
    Tolerance Intervals and Prediction Intervals
    175(10)
    Experimental Design
    185(12)
    Sizing the Experiment
    197(18)
    Analysis of Variance to Compare k Averages
    215(8)
    Components of Variance
    223(10)
    Multiple Factor Analysis of Variance
    233(6)
    Factorial Experimental Designs
    239(10)
    Fractional Factorial Experimental Designs
    249(12)
    Screening of Important Variables
    261(10)
    Analyzing Factorial Experiments by Regression
    271(10)
    Correlation
    281(8)
    Serial Correlation
    289(6)
    The Method of Least Squares
    295(8)
    Precision of Parameter Estimates in Linear Models
    303(8)
    Precision of Parameter Estimates in Nonlinear Models
    311(8)
    Calibration
    319(8)
    Weighted Least Squares
    327(10)
    Empirical Model Building by Linear Regression
    337(8)
    The Coefficient of Determination, R2
    345(10)
    Regression Analysis with Categorical Variables
    355(10)
    The Effect of Autocorrelation on Regression
    365(8)
    The Iterative Approach to Experimentation
    373(6)
    Seeking Optimum Conditions by Response Surface Methodology
    379(10)
    Designing Experiments for Nonlinear Parameter Estimation
    389(8)
    Why Linearization Can Bias Parameter Estimates
    397(6)
    Fitting Models to Multiresponse Data
    403(8)
    A Problem in Model Discrimination
    411(8)
    Data Adjustment for Process Rationalization
    419(6)
    How Measurement Errors are Transmitted into Calculated Values
    425(8)
    Using Simulation to Study Statistical Problems
    433(8)
    Introduction to Time Series Modeling
    441(12)
    Transfer Function Models
    453(6)
    Forecasting Time Series
    459(8)
    Intervention Analysis
    467(10)
    Appendix --- Statistical Tables 477(4)
    Index 481
    Linfield C. Brown, Paul Mac Berthouex