Statistical methods in water resources
著者
書誌事項
Statistical methods in water resources
(Studies in environmental science, 49)
Elsevier, 1992
大学図書館所蔵 全8件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Bibliography: p. [433]-449
内容説明・目次
内容説明
Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.
目次
1. Summarizing Data. 1.1. Characteristics of Water Resources Data. 1.2.Measures of Location. 1.3. Measures of Spread. 1.4. Measures of Skewness. 1.5.Other Resistant Measures. 1.6. Outliers. 1.7. Transformations. 2. GraphicalData Analysis. 2.1. Graphical Analysis of Single Data Sets. 2.2. GraphicalComparisons of Two or More Data Sets. 2.3. Scatterplots and Enhancements. 2.4.Graphs for Multivariate Data. 3. Describing Uncertainty. 3.1. Definitionof Interval Estimates. 3.2. Interpretation of Interval Estimates. 3.3.Confidence Intervals For The Median. 3.4. Confidence Intervals For The Mean.3.5. Nonparametric Prediction Intervals. 3.6. Parametric Prediction Intervals.3.7. Confidence Intervals For Quantiles (Percentiles). 3.8. Other Uses ForConfidence Intervals. 4. Hypothesis Tests. 4.1. Classification ofHypothesis Tests. 4.2. Structure of Hypothesis Tests. 4.3. The Rank-Sum Testas an Example of Hypothesis Testing. 4.4. Tests for Normality. 5.Differences Between Two Independent Groups. 5.1. The Rank-Sum Test. 5.2.The t-Test. 5.3. Graphical Presentation of Results. 5.4. Estimating theMagnitude of Differences Between Two Groups. 6. Matched-Pair Tests.6.1. The Sign Test. 6.2. The Signed-Rank Test. 6.3. The Paired t-Test. 6.4.Consequences of Violating Test Assumptions. 6.5. Graphical Presentation ofResults. 6.6. Estimating the Magnitude of Differences Between Two Groups.7. Comparing Several Independent Groups. 7.1. Tests for Differences Dueto One Factor. 7.2. Tests For The Effects of More Than One Factor. 7.3.Blocking - The Extension of Matched-Pair Tests. 7.4. Multiple ComparisonTests. 7.5. Presentation of Results. 8. Correlation. 8.1.Characteristics of Correlation Coefficients. 8.2. Kendall's Tau. 8.3.Spearman's Rho. 8.4. Pearson's r. 9. Simple Linear Regression. 9.1. TheLinear Regression Model. 9.2. Computations. 9.3. Building a Good RegressionModel. 9.4. Hypothesis Testing in Regression. 9.5. Regression Diagnostics.9.6. Transformations of the Response (y) Variable. 9.7. Summary Guide to aGood SLR Model. 10. Alternative Methods to Regression. 10.1. Kendall-Theil Robust Line. 10.2. Alternative Parametric Linear Equations. 10.3.Weighted Least Squares. 10.4 Iteratively Weighted Least Squares. 10.5.Smoothing. 11. Multiple Regression. 11.1. Why Use MLR? 11.2. MLR Model.11.3. Hypothesis Tests for Multiple Regression. 11.4. Confidence Intervals.11.5. Regression Diagnostics. 11.6. Choosing the Best MLR Model. 11.7. Summaryof Model Selection Criteria. 11.8. Analysis of Covariance. 12. TrendAnalysis. 12.1. General Structure of Trend Tests. 12.2. Trend Tests With NoExogenous Variable. 12.3. Accounting for Exogenous Variables. 12.4. DealingWith Seasonality. 12.5. Use of Transformations in Trend Studies. 12.6.Monotonic Trend versus Two Sample (Step) Trend. 12.7. Applicability of TrendTests With Censored Data. 13. Methods for Data Below the ReportingLimit. 13.1. Methods for Estimating Summary Statistics. 13.2. Methods forHypothesis Testing. 13.3. Methods for Regression With Censored Data. 14.Discrete Relationships. 14.1. Recording Categorical Data. 14.2.Contingency Tables (Both Variables Nominal). 14.3. Kruskal-Wallis Test forOrdered Categorical Responses. 14.4. Kendall's Tau for Categorical Data (BothVariables Ordinal). 14.5. Other Methods for Analysis of Categorical Data.15. Regression for Discrete Responses. 15.1. Regression For BinaryResponse Variables. 15.2. Logistic Regression. 15.3. Alternatives to LogisticRegression. 15.4. Logistic Regression for More Than Two Response Categories.16. Presentation Graphics. 16.1. The Value of Presentation Graphics.16.2. Precision of Graphs. 16.3. Misleading Graphics To Be Avoided.References. Appendix A: Construction of Boxplots. Appendix B: Tables.Appendix C: Data Sets. Appendix D: Answers to Exercises.
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