Environmental and ecological statistics with R / Song S. Qian.

Saved in:
Bibliographic Details
Online Access: Full Text (via EBSCO)
Main Author: Qian, Song S. (Author)
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, [2017]
Edition:Second edition.
Series:Applied environmental statistics.
Subjects:

MARC

LEADER 00000cam a2200000xi 4500
001 b9131067
006 m o d
007 cr |||||||||||
008 161109s2017 flu ob 001 0 eng d
005 20240520201529.0
019 |a 962325816  |a 963353337  |a 964321057 
020 |a 9781498728737  |q (electronic bk.) 
020 |a 1498728731  |q (electronic bk.) 
020 |a 1498728723  |q (hardback ;  |q alk. paper) 
020 |a 9781498728720  |q (hardback ;  |q alk. paper) 
020 |a 9781498728751  |q (e-book) 
020 |a 1498728758  |q (e-book) 
020 |a 9781498728744  |q (e-book) 
020 |a 149872874X  |q (e-book) 
020 |z 9781498728720 
035 |a (OCoLC)ebs962303405 
035 |a (OCoLC)962303405  |z (OCoLC)962325816  |z (OCoLC)963353337  |z (OCoLC)964321057 
037 |a ebs1410098 
040 |a N$T  |b eng  |e rda  |e pn  |c N$T  |d IDEBK  |d N$T  |d CNCGM  |d OCLCF  |d OCLCQ  |d YDX  |d OCLCQ  |d NJR  |d UPM  |d OCLCQ  |d AU@  |d ZCU 
049 |a GWRE 
050 4 |a GE45.S73  |b Q25 2017 
066 |c (S 
100 1 |a Qian, Song S.,  |e author.  |0 http://id.loc.gov/authorities/names/n2009041027  |1 http://isni.org/isni/0000000064476068. 
245 1 0 |a Environmental and ecological statistics with R /  |c Song S. Qian. 
250 |a Second edition. 
264 1 |a Boca Raton, FL :  |b CRC Press,  |c [2017] 
264 4 |c ©2017. 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent. 
337 |a computer  |b c  |2 rdamedia. 
338 |a online resource  |b cr  |2 rdacarrier. 
490 1 |a Chapman & Hall/CRC Press applied environmental statistics. 
504 |a Includes bibliographical references and index. 
505 0 |a Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- List of Figures -- List of Tables -- I: Basic Concepts -- 1: Introduction -- 1.1 Tool for Inductive Reasoning -- 1.2 The Everglades Example -- 1.2.1 Statistical Issues -- 1.3 Effects of Urbanization on Stream Ecosystems -- 1.3.1 Statistical Issues -- 1.4 PCB in Fish from Lake Michigan -- 1.4.1 Statistical Issues -- 1.5 Measuring Harmful Algal Bloom Toxin -- 1.6 Bibliography Notes -- 1.7 Exercise -- 2: A Crash Course on R -- 2.1 What is R? -- 2.2 Getting Started with R -- 2.2.1 R Commands and Scripts -- 2.2.2 R Packages -- 2.2.3 R Working Directory -- 2.2.4 Data Types -- 2.2.5 R Functions -- 2.3 Getting Data into R -- 2.3.1 Functions for Creating Data -- 2.3.2 A Simulation Example -- 2.4 Data Preparation -- 2.4.1 Data Cleaning -- 2.4.1.1 Missing Values -- 2.4.2 Subsetting and Combining Data -- 2.4.3 Data Transformation -- 2.4.4 Data Aggregation and Reshaping -- 2.4.5 Dates -- 2.5 Exercises -- 3: Statistical Assumptions -- 3.1 The Normality Assumption -- 3.2 The Independence Assumption -- 3.3 The Constant Variance Assumption -- 3.4 Exploratory Data Analysis -- 3.4.1 Graphs for Displaying Distributions -- 3.4.2 Graphs for Comparing Distributions -- 3.4.3 Graphs for Exploring Dependency among Variables -- 3.5 From Graphs to Statistical Thinking -- 3.6 Bibliography Notes -- 3.7 Exercises -- 4: Statistical Inference -- 4.1 Introduction -- 4.2 Estimation of Population Mean and Confidence Interval -- 4.2.1 Bootstrap Method for Estimating Standard Error -- 4.3 Hypothesis Testing -- 4.3.1 t-Test -- 4.3.2 Two-Sided Alternatives -- 4.3.3 Hypothesis Testing Using the Confidence Interval -- 4.4 A General Procedure -- 4.5 Nonparametric Methods for Hypothesis Testing -- 4.5.1 Rank Transformation. 
505 8 |6 880-01  |a 7: Classification and Regression Tree -- 7.1 The Willamette River Example -- 7.2 Statistical Methods -- 7.2.1 Growing and Pruning a Regression Tree -- 7.2.2 Growing and Pruning a Classification Tree -- 7.2.3 Plotting Options -- 7.3 Comments -- 7.3.1 CART as a Model Building Tool -- 7.3.2 Deviance and Probabilistic Assumptions -- 7.3.3 CART and Ecological Threshold -- 7.4 Bibliography Notes -- 7.5 Exercises -- 8: Generalized Linear Model -- 8.1 Logistic Regression -- 8.1.1 Example: Evaluating the Effectiveness of UV as a Drinking Water Disinfectant -- 8.1.2 Statistical Issues -- 8.1.3 Fitting the Model in R -- 8.2 Model Interpretation -- 8.2.1 Logit Transformation -- 8.2.2 Intercept -- 8.2.3 Slope -- 8.2.4 Additional Predictors -- 8.2.5 Interaction -- 8.2.6 Comments on the Crypto Example -- 8.3 Diagnostics -- 8.3.1 Binned Residuals Plot -- 8.3.2 Overdispersion -- 8.3.3 Seed Predation by Rodents: A Second Example of Logistic Regression -- 8.4 Poisson Regression Model -- 8.4.1 Arsenic Data from Southwestern Taiwan -- 8.4.2 Poisson Regression -- 8.4.3 Exposure and Offset -- 8.4.4 Overdispersion -- 8.4.5 Interactions -- 8.4.6 Negative Binomial -- 8.5 Multinomial Regression -- 8.5.1 Fitting a Multinomial Regression Model in R -- 8.5.2 Model Evaluation -- 8.6 The Poisson-Multinomial Connection -- 8.7 Generalized Additive Models -- 8.7.1 Example: Whales in the Western Antarctic Peninsula -- 8.7.1.1 The Data -- 8.7.1.2 Variable Selection Using CART -- 8.7.1.3 Fitting GAM -- 8.7.1.4 Summary -- 8.8 Bibliography Notes -- 8.9 Exercises -- III: Advanced Statistical Modeling -- 9: Simulation for Model Checking and Statistical Inference -- 9.1 Simulation -- 9.2 Summarizing Regression Models Using Simulation -- 9.2.1 An Introductory Example -- 9.2.2 Summarizing a Linear Regression Model -- 9.2.2.1 Re-transformation Bias. 
505 8 |a 9.2.3 Simulation for Model Evaluation -- 9.2.4 Predictive Uncertainty -- 9.3 Simulation Based on Re-sampling -- 9.3.1 Bootstrap Aggregation -- 9.3.2 Example: Confidence Interval of the CART-Based Threshold -- 9.4 Bibliography Notes -- 9.5 Exercises -- 10: Multilevel Regression -- 10.1 From Stein's Paradox to Multilevel Models -- 10.2 Multilevel Structure and Exchangeability -- 10.3 Multilevel ANOVA -- 10.3.1 Intertidal Seaweed Grazers -- 10.3.2 Background N2O Emission from Agriculture Fields -- 10.3.3 When to Use the Multilevel Model? -- 10.4 Multilevel Linear Regression -- 10.4.1 Nonnested Groups -- 10.4.2 Multiple Regression Problems -- 10.4.3 The ELISA Example-An Unintended Multilevel Modeling Problem -- 10.5 Nonlinear Multilevel Models -- 10.6 Generalized Multilevel Models -- 10.6.1 Exploited Plant Monitoring-Galax -- 10.6.1.1 A Multilevel Poisson Model -- 10.6.1.2 A Multilevel Logistic Regression Model -- 10.6.2 Cryptosporidium in U.S. Drinking Water-A Poisson Regression Example -- 10.6.3 Model Checking Using Simulation -- 10.7 Concluding Remarks -- 10.8 Bibliography Notes -- 10.9 Exercises -- 11: Evaluating Models Based on Statistical Signicance Testing -- 11.1 Introduction -- 11.2 Evaluating TITAN -- 11.2.1 A Brief Description of TITAN -- 11.2.2 Hypothesis Testing in TITAN -- 11.2.3 Type I Error Probability -- 11.2.4 Statistical Power -- 11.2.5 Bootstrapping -- 11.2.6 Community Threshold -- 11.2.7 Conclusions -- 11.3 Exercises -- Bibliography -- Index. 
588 0 |a Online resource; title from PDF title page (EBSCO, viewed December 5, 2016) 
650 0 |a Environmental sciences  |x Statistical methods.  |0 http://id.loc.gov/authorities/subjects/sh2009103465. 
650 0 |a Ecology  |x Statistical methods.  |0 http://id.loc.gov/authorities/subjects/sh2008118584. 
650 0 |a R (Computer program language)  |0 http://id.loc.gov/authorities/subjects/sh2002004407. 
650 7 |a Ecology  |x Statistical methods.  |2 fast  |0 (OCoLC)fst00901539. 
650 7 |a Environmental sciences  |x Statistical methods.  |2 fast  |0 (OCoLC)fst00913513. 
650 7 |a R (Computer program language)  |2 fast  |0 (OCoLC)fst01086207. 
776 0 8 |i Print version:  |a Qian, Song S.  |t Environmental and ecological statistics with R.  |b Second edition.  |d Boca Raton, FL : CRC Press, [2017]  |z 9781498728720  |z 1498728723  |w (DLC) 2016022441. 
830 0 |a Applied environmental statistics.  |0 http://id.loc.gov/authorities/names/n00013775. 
856 4 0 |u https://colorado.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&site=ehost-live&AN=1410098  |z Full Text (via EBSCO) 
880 8 |6 505-01/(S  |a 4.5.2 Wilcoxon Signed Rank Test -- 4.5.3 Wilcoxon Rank Sum Test -- 4.5.4 A Comment on Distribution-Free Methods -- 4.6 Significance Level α, Power 1 -- β, and p-Value -- 4.7 One-Way Analysis of Variance -- 4.7.1 Analysis of Variance -- 4.7.2 Statistical Inference -- 4.7.3 Multiple Comparisons -- 4.8 Examples -- 4.8.1 The Everglades Example -- 4.8.2 Kemp's Ridley Turtles -- 4.8.3 Assessing Water Quality Standard Compliance -- 4.8.4 Interaction between Red Mangrove and Sponges -- 4.9 Bibliography Notes -- 4.10 Exercises -- II: Statistical Modeling -- 5: Linear Models -- 5.1 Introduction -- 5.2 From t-test to Linear Models -- 5.3 Simple and Multiple Linear Regression Models -- 5.3.1 The Least Squares -- 5.3.2 Regression with One Predictor -- 5.3.3 Multiple Regression -- 5.3.4 Interaction -- 5.3.5 Residuals and Model Assessment -- 5.3.6 Categorical Predictors -- 5.3.7 Collinearity and the Finnish Lakes Example -- 5.4 General Considerations in Building a Predictive Model -- 5.5 Uncertainty in Model Predictions -- 5.5.1 Example: Uncertainty in Water Quality Measurements -- 5.6 Two-Way ANOVA -- 5.6.1 ANOVA as a Linear Model -- 5.6.2 More Than One Categorical Predictor -- 5.6.3 Interaction -- 5.7 Bibliography Notes -- 5.8 Exercises -- 6: Nonlinear Models -- 6.1 Nonlinear Regression -- 6.1.1 Piecewise Linear Models -- 6.1.2 Example: U.S. Lilac First Bloom Dates -- 6.1.3 Selecting Starting Values -- 6.2 Smoothing -- 6.2.1 Scatter Plot Smoothing -- 6.2.2 Fitting a Local Regression Model -- 6.3 Smoothing and Additive Models -- 6.3.1 Additive Models -- 6.3.2 Fitting an Additive Model -- 6.3.3 Example: The North American Wetlands Database -- 6.3.4 Discussion: The Role of Nonparametric Regression Models in Science -- 6.3.5 Seasonal Decomposition of Time Series -- 6.3.5.1 The Neuse River Example -- 6.4 Bibliographic Notes -- 6.5 Exercises. 
907 |a .b91310672  |b 08-18-22  |c 05-31-17 
907 |a .b91310672  |b 01-04-21  |c 05-31-17 
915 |a I 
944 |a MARS - RDA ENRICHED 
956 |a EBSCO ebook collection 
956 |b All EBSCO eBooks 
998 |a web  |b 12-31-20  |c b  |d b   |e -  |f eng  |g flu  |h 0  |i 1 
999 f f |i 14da04ae-4d76-5a0d-9528-ea8748e1790a  |s f2130439-0cc7-5145-bad7-1c1520406f6d 
952 f f |p Can circulate  |a University of Colorado Boulder  |b Online  |c Online  |d Online  |e GE45.S73 Q25 2017  |h Library of Congress classification  |i web  |n 1