| 000 | 00872camuu2200241 a 4500 | |
| 001 | 000045231059 | |
| 005 | 20060214163856 | |
| 008 | 020606s2002 flua b 001 0 eng | |
| 010 | ▼a 2002282889 | |
| 020 | ▼a 1566705924 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 244002 | |
| 082 | 0 0 | ▼a 001.4/22/024628 ▼2 21 |
| 090 | ▼a 001.422 ▼b B542s2 | |
| 100 | 1 | ▼a Berthouex, P. Mac ▼q (Paul Mac), ▼d 1940-. |
| 245 | 1 0 | ▼a Statistics for environmental engineers / ▼c Paul Mac Berthouex, Linfield C. Brown. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Boca Raton : ▼b Lewis Publishers, ▼c c2002. | |
| 300 | ▼a 489 p. : ▼b ill. ; ▼c 27 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Environmental engineering ▼x Statistical methods. |
| 700 | 1 | ▼a Brown, Linfield C. |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/인문자료실1(2층)/ | 청구기호 001.422 B542s2 | 등록번호 151195268 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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:
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.
The second edition of this bestseller is an ideal textbook for students and a valuable reference for environmental scientists and engineers. 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 so that each one can 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.
정보제공 :
목차
Environmental Problems and Statistics
A Brief Review of Statistics
Plotting Data
Smoothing Data
Seeing the Shape of a Distribution
External Reference Distributions
Using Transformations
Estimating Percentiles
Accuracy, Bias, and Precision of Measurements
Precision of Calculated Values
Laboratory Quality Assurance
Fundamentals of Process Control Charts
Specialized Control Charts
Limit of Detection
Analyzing Censored Data
Comparing a Mean with a Standard
Paired t -Test for Assessing the Average of Differences
Independent t-Test for Assessing the Difference of Two Averages
Assessing the Difference of Proportions
Multiple Paired Comparison of k Averages
Tolerance Intervals and Prediction Intervals
Experimental Design
Sizing the Experiment
Analysis of Variance to Compare k Averages
Components of Variance
Multiple Factor Analysis of Variance
Factorial Experimental Designs
Fractional Factorial Experimental Designs
Screening of Important Variables
Analyzing Factorial Experiments by Regression
Correlation
Serial Correlation
The Method of Least Squares
Precision of Parameter Estimates in Linear Models
Precision of Parameter Estimates in Nonlinear Models
Calibration
Weighted Least Squares
Empirical Model Building by Linear Regression
The Coefficient of Determination, R2
Regression Analysis with Categorical Variables
The Effect of Autocorrelation on Regression
The Iterative Approach to Experimentation
Seeking OptimumConditions by Response Surface Methodology
Designing Experiments for Nonlinear Parameter Estimation
Why Linearization Can Bias Parameter Estimates
A Problem in Fitting Models to Multiresponse Data
Model Discrimination
Data Adjustment for Process Rationalization
How Measurement Errors are Transmitted into Calculated Values
Using Simulations to Study Statistical Problems
Introduction to Time Series Modeling
Transfer Function Models
Forecasting Time Series
Intervention Analysis
Appendix-Statistical Tables
Index
정보제공 :