LEADER 01143nam a2200325 i 4500 001 991002010219707536 005 20020508184959.0 008 991102s1996 it 000 0 ita 020 $a8871445775 035 $ab10945945-39ule_inst 035 $aPARLA152843$9ExL 040 $aDipart. Scienze pedagogiche$bita 082 0 $a371.91 100 1 $aJadoulle, Andréa$0482260 245 10$aApprendimento della lettura e dislessia /$cAndréa Jadoulle 250 $a7. rist. 260 $aRoma :$bArmando,$c1996 300 $a253 p. ;$c24 cm. 490 0 $aCollana medico-pedagogica 500 $aTit orig.: Apprentissage de la lecture et dyslexie 500 $aTrad. it. di Itala Brusa 650 4$aFanciulli dislessici$xRieducazione 650 4$aLettura$xInsegnamento 700 1 $aBrusa, Itala 907 $a.b10945945$b08-01-18$c28-06-02 912 $a991002010219707536 945 $aLE022 371 JAD01.01$g1$i2022000008471$lle022$o-$pE0.00$q-$rl$s- $t0$u15$v12$w15$x0$y.i11053203$z28-06-02 996 $aApprentissage de la lecture et dyslexie$926883 997 $aUNISALENTO 998 $ale022$b01-01-99$cm$da $e-$fita$git $h0$i1 LEADER 05356nam 2200625 450 001 9910826753703321 005 20200520144314.0 010 $a1-118-63446-2 035 $a(CKB)2550000001298079 035 $a(EBL)1687058 035 $a(MiAaPQ)EBC1687058 035 $a(Au-PeEL)EBL1687058 035 $a(CaPaEBR)ebr10870841 035 $a(CaONFJC)MIL608497 035 $a(OCoLC)880058470 035 $a(MiAaPQ)EBC7103908 035 $a(Au-PeEL)EBL7103908 035 $a(JP-MeL)3000110608 035 $a(EXLCZ)992550000001298079 100 $a20140522h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aStatistical applications for environmental analysis and risk assessment /$fJoseph Ofungwu 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons,$d2014. 210 4$d2014 215 $a1 online resource (648 p.) 225 1 $aWiley Series in Statistics in Practice 300 $aIncludes bibliographical references (p. 609-612) and index 311 $a1-118-63453-5 311 $a1-306-77246-X 320 $aIncludes bibliographical references and index. 327 $aStatistical Applications for Environmental Analysis and Risk Assessment; Contents; Preface; Acknowledgements; 1. Introduction; 1.1 Introduction and Overview; 1.2 The Aim of the Book: Get Involved!; 1.3 The Approach and Style: Clarity, Clarity, Clarity; Part I: Basic Statistical Measures and Concepts; 2. Introduction to Software Packages used in this Book; 2.1 R; 2.1.1 Helpful R Tips; 2.1.2 Disadvantages of R; 2.2 ProUCL; 2.2.1 Helpful ProUCL Tips; 2.2.2 Potential Deficiencies of ProUCL; 2.3 Visual Sample Plan; 2.4 DATAPLOT; 2.4.1 Helpful Tips for Running DATAPLOT in Batch Mode 327 $a2.5 Kendall-Thiel Robust Line2.6 Minitab®; 2.7 Microsoft Excel; 3. Laboratory Detection Limits, Non-Detects and Data Analysis; 3.1 Introduction and Overview; 3.2 Types of Laboratory Data Detection Limits; 3.3 Problems with Nondetects in Statistical Data Samples; 3.4 Options for Addressing Nondetects in Data Analysis; 3.4.1 Kaplan-Meier Estimation; 3.4.2 Robust Regression on Order Statistics; 3.4.3 Maximum Likelihood Estimation; 4. Data Sample, Data Population and Data Distribution; 4.1 Introduction and Overview; 4.2 Data Sample Versus Data Population or Universe 327 $a4.3 The Concept of a Distribution4.3.1 The Concept of a Probability Distribution Function; 4.3.2 Cumulative Probability Distribution and Empirical Cumulative Distribution Functions; 4.4 Types of Distributions; 4.4.1 Normal Distribution; 4.4.1.1 Goodness-of-Fit (GOF) Tests for the Normal Distribution; 4.4.1.2 Central Limit Theorem; 4.4.2 Lognormal, Gamma, and Other Continuous Distributions; 4.4.2.1 Gamma Distribution; 4.4.2.2 Logistic Distribution; 4.4.2.3 Other Continuous Distributions; 4.4.3 Distributions Used in Inferential Statistics (Student's t, Chi-Square, F) 327 $a4.4.3.1 Student's t Distribution4.4.3.2 Chi-Square Distribution; 4.4.3.3 F Distribution; 4.4.4 Discrete Distributions; 4.4.4.1 Binomial Distribution; 4.4.4.2 Poisson Distribution; Exercises; 5. Graphics for Data Analysis and Presentation; 5.1 Introduction and Overview; 5.2 Graphics for Single Univariate Data Samples; 5.2.1 Box and Whiskers Plot; 5.2.2 Probability Plots (i.e., Quantile-Quantile Plots for Comparing a Data Sample to a Theoretical Distribution); 5.2.3 Quantile Plots; 5.2.4 Histograms and Kernel Density Plots; 5.3 Graphics for Two or More Univariate Data Samples 327 $a5.3.1 Quantile-Quantile Plots for Comparing Two Univariate Data Samples5.3.2 Side-by-Side Box Plots; 5.4 Graphics for Bivariate and Multivariate Data Samples; 5.4.1 Graphical Data Analysis for Bivariate Data Samples; 5.4.2 Graphical Data Analysis for Multivariate Data Samples; 5.5 Graphics for Data Presentation; 5.6 Data Smoothing; 5.6.1 Moving Average and Moving Median Smoothing; 5.6.2 Locally Weighted Scatterplot Smoothing (LOWESS or LOESS); 5.6.2.1 Smoothness Factor and the Degree of the Local Regression; 5.6.2.2 Basic and Robust LOWESS Weighting Functions 327 $a5.6.2.3 LOESS Scatterplot Smoothing for Data with Multiple Variables 330 $aStatistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "ready-made" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes: Descriptions of basic statistical concepts and principles in an informal style t 410 0$aStatistics in practice. 606 $aEnvironmental risk assessment$xStatistical methods 615 0$aEnvironmental risk assessment$xStatistical methods. 676 $a363.7/02 686 $a519.15$2njb/09 686 $a363.7/02$2njb/09 700 $aOfungwu$b Joseph$01718203 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826753703321 996 $aStatistical applications for environmental analysis and risk assessment$94114992 997 $aUNINA