2nd Edition
Statistics for Environmental Engineers
502 Pages
193 B/W Illustrations
by
CRC Press
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... Read more
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
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
Biography
Linfield C. Brown, Paul Mac Berthouex
"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
