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Designing Open Access  E-Journals Website
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Designing Open Access E-Journals Website ab 58.99 € als Taschenbuch: Practical Concepts and Techniques. Aus dem Bereich: Bücher, Wissenschaft, Technik,

Anbieter: hugendubel
Stand: 29.09.2020
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3D Image Reconstruction for CT and PET (eBook, ...
12,95 € *
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This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.

Anbieter: buecher
Stand: 29.09.2020
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3D Image Reconstruction for CT and PET (eBook, ...
12,95 € *
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This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.

Anbieter: buecher
Stand: 29.09.2020
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3D Image Reconstruction for CT and PET (eBook, ...
12,95 € *
ggf. zzgl. Versand

This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.

Anbieter: buecher
Stand: 29.09.2020
Zum Angebot
3D Image Reconstruction for CT and PET (eBook, ...
12,95 € *
ggf. zzgl. Versand

This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of guiding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction. A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the rigor of mathematical background Accompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press website Ideal for those willing to move their first steps on the real practice of image reconstruction, with modern scientific programming language and toolsets Daniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa. From 2005 to 2007, he worked at the Department of Physics "E. Fermi" of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa. Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the field of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teaching courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, Journal of Biomedical and Health Informatics.

Anbieter: buecher
Stand: 29.09.2020
Zum Angebot
A Second Course in Statistics: Pearson New Inte...
57,99 € *
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The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich’s A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course. A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion. Features + Benefits Readability was a main goal of the authors, whose intent was to create a teaching text rather than a reference text. Concepts are explained in a logical, intuitive manner with worked-out examples. Model building is fundamental to any regression analysis and is introduced in Chapters 4—8, then emphasized throughout the text. Development of regression skills: in addition to teaching basic concepts and methodology, this text stresses its usage in solving applied problems. Real data is used in examples and exercises to maintain the applied nature of this text. Examples illustrate the important aspects of model construction, data analysis, and the interpretation of results. Exercises are located at the end of every section and chapter. Nearly every exercise is based on data and research extracted from news or journal articles. Seven case studies address real-life research problems and are suitable for class discussion. While working through these, students can see how regression analysis is used to answer practical questions, and can then formulate appropriate statistical models for the analysis and interpretation of sample data. Data sets for all case studies, exercises, and examples are available on the CD-ROM included with the book and on the Pearson Data Sets website (www.pearsonhighered.com/datasets ). Statistical software instruction includes the latest software packages: SAS®, SPSS®, MINITAB®, and, new to this edition, R. Tutorials are provided on the included CD-ROM and printouts associated with the software are presented and discussed throughout the text. 1. A Review of Basic Concepts (Optional) 1.1 Statistics and Data 1.2 Populations, Samples, and Random Sampling 1.3 Describing Qualitative Data 1.4 Describing Quantitative Data Graphically 1.5 Describing Quantitative Data Numerically 1.6 The Normal Probability Distribution 1.7 Sampling Distributions and the Central Limit Theorem 1.8 Estimating a Population Mean 1.9 Testing a Hypothesis About a Population Mean 1.10 Inferences About the Difference Between Two Population Means 1.11 Comparing Two Population Variances 2. Introduction to Regression Analysis 2.1 Modeling a Response 2.2 Overview of Regression Analysis 2.3 Regression Applications 2.4 Collecting the Data for Regression 3. Simple Linear Regression 3.1 Introduction 3.2 The Straight-Line Probabilistic Model 3.3 Fitting the Model: The Method of Least Squares 3.4 Model Assumptions 3.5 An Estimator of s2 3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1 3.7 The Coefficient of Correlation 3.8 The Coefficient of Determination 3.9 Using the Model for Estimation and Prediction 3.10 A Complete Example 3.11 Regression Through the Origin (Optional) Case Study 1: Legal Advertising--Does It Pay? 4. Multiple Regression Models 4.1 General Form of a Multiple Regression Model 4.2 Model Assumptions 4.3 A First-Order Model with Quantitative Predictors 4.4 Fitting the Model: The Method of Least Squares 4.5 Estimation of s2, the Variance of e 4.6 Testing the Utility of a Model: The Analysis of Variance F-Test 4.7 Inferences About the Individual ß Parameters 4.8 Multiple Coefficients of Determination: R2 and R2adj 4.9 Using the Model for Estimation and Prediction 4.10 An Interaction Model with Quantitative Predictors 4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor 4.12 More Complex Multiple Regression Models (Optional) 4.13 A Test for Comparing Nested Models 4.14 A Complete Example Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods 5. Principles of Model Building 5.1 Introduction: Why Model Building is Important 5.2 The Two Types of Independent Variables: Quantitative and Qualitative 5.3 Models with a Single Quantitative Independent Variable 5.4 First-Order Models with Two or More Quantitative Independent Variables 5.5 Second-Order Models with Two or More Quantitative Independent Variables 5.6 Coding Quantitative Independent Variables (Optional) 5.7 Models with One Qualitative Independent Variable 5.8 Models with Two Qualitative Independent Variables 5.9 Models with Three or More Qualitative Independent Variables 5.10 Models with Both Quantitative and Qualitative Independent Variables 5.11 External Model Validation 6. Variable Screening Methods 6.1 Introduction: Why Use a Variable-Screening Method? 6.2 Stepwise Regression 6.3 All-Possible-Regressions Selection Procedure 6.4 Caveats Case Study 3: Deregulation of the Intrastate Trucking Industry 7. Some Regression Pitfalls 7.1 Introduction 7.2 Observational Data Versus Designed Experiments 7.3 Parameter Estimability and Interpretation 7.4 Multicollinearity 7.5 Extrapolation: Predicting Outside the Experimental Region 7.6 Variable Transformations 8. Residual Analysis 8.1 Introduction 8.2 Plotting Residuals 8.3 Detecting Lack of Fit 8.4 Detecting Unequal Variances 8.5 Checking the Normality Assumption 8.6 Detecting Outliers and Identifying Influential Observations 8.7 Detection of Residual Correlation: The Durbin-Watson Test Case Study 4: An Analysis of Rain Levels in California Case Study 5: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction 9. Special Topics in Regression (Optional) 9.1 Introduction 9.2 Piecewise Linear Regression 9.3 Inverse Prediction 9.4 Weighted Least Squares 9.5 Modeling Qualitative Dependent Variables 9.6 Logistic Regression 9.7 Ridge Regression 9.8 Robust Regression 9.9 Nonparametric Regression Models 10. Introduction to Time Series Modeling and Forecasting 10.1 What is a Time Series? 10.2 Time Series Components 10.3 Forecasting Using Smoothing Techniques (Optional) 10.4 Forecasting: The Regression Approach 10.5 Autocorrelation and Autoregressive Error Models 10.6 Other Models for Autocorrelated Errors (Optional) 10.7 Constructing Time Series Models 10.8 Fitting Time Series Models with Autoregressive Errors 10.9 Forecasting with Time Series Autoregressive Models 10.10 Seasonal Time Series Models: An Example 10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional) Case Study 6: Modeling Daily Peak Electricity Demands 11. Principles of Experimental Design 11.1 Introduction 11.2 Experimental Design Terminology 11.3 Controlling the Information in an Experiment 11.4 Noise-Reducing Designs 11.5 Volume-Increasing Designs 11.6 Selecting the Sample Size 11.7 The Importance of Randomization 12. The Analysis of Variance for Designed Experiments 12.1 Introduction 12.2 The Logic Behind an Analysis of Variance 12.3 One-Factor Completely Randomized Designs 12.4 Randomized Block Designs 12.5 Two-Factor Factorial Experiments 12.6 More Complex Factorial Designs (Optional) 12.7 Follow-Up Analysis: Tukey's Multiple Comparisons of Means 12.8 Other Multiple Comparisons Methods (Optional) 12.9 Checking ANOVA Assumptions Case Study 7: Reluctance to Transmit Bad News: The MUM Effect Appendix A: Derivation of the Least Squares Estimates of ß0 and ß1 in Simple Linear Regression Appendix B: The Mechanics of a Multiple Regression Analysis B.1 Introduction B.2 Matrices and Matrix Multiplication B.3 Identity Matrices and Matrix Inversion B.4 Solving Systems of Simultaneous Linear Equations B.5 The Least Squares Equations and Their Solution B.6 Calculating SSE and s2 B.7 Standard Errors of Estimators, Test Statistics, and Confidence Intervals for ß0, ß1, ... , ßk B.8 A Confidence Interval for a Linear Function of the ß Parameters; A Confidence Interval for E(y) B.9 A Prediction Interval for Some Value of y to be Observed in the Future Appendix C: A Procedure for Inverting a Matrix Appendix D: Statistical Tables Table D.1: Normal Curve Areas Table D.2: Critical Values for Student's t Table D.3: Critical Values for the F Statistic: F.10 Table D.4: Critical Values for the F Statistic: F.05 Table D.5: Critical Values for the F Statistic: F.025 Table D.6: Critical Values for the F Statistic: F.01 Table D.7: Random Numbers Table D.8: Critical Values for the Durbin-Watson d Statistic (a =.05) Table D.9: Critical Values for the Durbin-Watson d Statistic (a =.01) Table D.10: Critical Values for the X2-Statistic Table D.11: Percentage Points of the Studentized Range, q(p,v), Upper 5% Table D.12: Percentage Points of the Studentized Range, q(p,v), Upper 1%The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich s A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course.A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion.

Anbieter: buecher
Stand: 29.09.2020
Zum Angebot
A Second Course in Statistics: Pearson New Inte...
57,99 € *
ggf. zzgl. Versand

The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich’s A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course. A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion. Features + Benefits Readability was a main goal of the authors, whose intent was to create a teaching text rather than a reference text. Concepts are explained in a logical, intuitive manner with worked-out examples. Model building is fundamental to any regression analysis and is introduced in Chapters 4—8, then emphasized throughout the text. Development of regression skills: in addition to teaching basic concepts and methodology, this text stresses its usage in solving applied problems. Real data is used in examples and exercises to maintain the applied nature of this text. Examples illustrate the important aspects of model construction, data analysis, and the interpretation of results. Exercises are located at the end of every section and chapter. Nearly every exercise is based on data and research extracted from news or journal articles. Seven case studies address real-life research problems and are suitable for class discussion. While working through these, students can see how regression analysis is used to answer practical questions, and can then formulate appropriate statistical models for the analysis and interpretation of sample data. Data sets for all case studies, exercises, and examples are available on the CD-ROM included with the book and on the Pearson Data Sets website (www.pearsonhighered.com/datasets ). Statistical software instruction includes the latest software packages: SAS®, SPSS®, MINITAB®, and, new to this edition, R. Tutorials are provided on the included CD-ROM and printouts associated with the software are presented and discussed throughout the text. 1. A Review of Basic Concepts (Optional) 1.1 Statistics and Data 1.2 Populations, Samples, and Random Sampling 1.3 Describing Qualitative Data 1.4 Describing Quantitative Data Graphically 1.5 Describing Quantitative Data Numerically 1.6 The Normal Probability Distribution 1.7 Sampling Distributions and the Central Limit Theorem 1.8 Estimating a Population Mean 1.9 Testing a Hypothesis About a Population Mean 1.10 Inferences About the Difference Between Two Population Means 1.11 Comparing Two Population Variances 2. Introduction to Regression Analysis 2.1 Modeling a Response 2.2 Overview of Regression Analysis 2.3 Regression Applications 2.4 Collecting the Data for Regression 3. Simple Linear Regression 3.1 Introduction 3.2 The Straight-Line Probabilistic Model 3.3 Fitting the Model: The Method of Least Squares 3.4 Model Assumptions 3.5 An Estimator of s2 3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1 3.7 The Coefficient of Correlation 3.8 The Coefficient of Determination 3.9 Using the Model for Estimation and Prediction 3.10 A Complete Example 3.11 Regression Through the Origin (Optional) Case Study 1: Legal Advertising--Does It Pay? 4. Multiple Regression Models 4.1 General Form of a Multiple Regression Model 4.2 Model Assumptions 4.3 A First-Order Model with Quantitative Predictors 4.4 Fitting the Model: The Method of Least Squares 4.5 Estimation of s2, the Variance of e 4.6 Testing the Utility of a Model: The Analysis of Variance F-Test 4.7 Inferences About the Individual ß Parameters 4.8 Multiple Coefficients of Determination: R2 and R2adj 4.9 Using the Model for Estimation and Prediction 4.10 An Interaction Model with Quantitative Predictors 4.11 A Quadratic (Second-Order) Model with a Quantitative Predictor 4.12 More Complex Multiple Regression Models (Optional) 4.13 A Test for Comparing Nested Models 4.14 A Complete Example Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods 5. Principles of Model Building 5.1 Introduction: Why Model Building is Important 5.2 The Two Types of Independent Variables: Quantitative and Qualitative 5.3 Models with a Single Quantitative Independent Variable 5.4 First-Order Models with Two or More Quantitative Independent Variables 5.5 Second-Order Models with Two or More Quantitative Independent Variables 5.6 Coding Quantitative Independent Variables (Optional) 5.7 Models with One Qualitative Independent Variable 5.8 Models with Two Qualitative Independent Variables 5.9 Models with Three or More Qualitative Independent Variables 5.10 Models with Both Quantitative and Qualitative Independent Variables 5.11 External Model Validation 6. Variable Screening Methods 6.1 Introduction: Why Use a Variable-Screening Method? 6.2 Stepwise Regression 6.3 All-Possible-Regressions Selection Procedure 6.4 Caveats Case Study 3: Deregulation of the Intrastate Trucking Industry 7. Some Regression Pitfalls 7.1 Introduction 7.2 Observational Data Versus Designed Experiments 7.3 Parameter Estimability and Interpretation 7.4 Multicollinearity 7.5 Extrapolation: Predicting Outside the Experimental Region 7.6 Variable Transformations 8. Residual Analysis 8.1 Introduction 8.2 Plotting Residuals 8.3 Detecting Lack of Fit 8.4 Detecting Unequal Variances 8.5 Checking the Normality Assumption 8.6 Detecting Outliers and Identifying Influential Observations 8.7 Detection of Residual Correlation: The Durbin-Watson Test Case Study 4: An Analysis of Rain Levels in California Case Study 5: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction 9. Special Topics in Regression (Optional) 9.1 Introduction 9.2 Piecewise Linear Regression 9.3 Inverse Prediction 9.4 Weighted Least Squares 9.5 Modeling Qualitative Dependent Variables 9.6 Logistic Regression 9.7 Ridge Regression 9.8 Robust Regression 9.9 Nonparametric Regression Models 10. Introduction to Time Series Modeling and Forecasting 10.1 What is a Time Series? 10.2 Time Series Components 10.3 Forecasting Using Smoothing Techniques (Optional) 10.4 Forecasting: The Regression Approach 10.5 Autocorrelation and Autoregressive Error Models 10.6 Other Models for Autocorrelated Errors (Optional) 10.7 Constructing Time Series Models 10.8 Fitting Time Series Models with Autoregressive Errors 10.9 Forecasting with Time Series Autoregressive Models 10.10 Seasonal Time Series Models: An Example 10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional) Case Study 6: Modeling Daily Peak Electricity Demands 11. Principles of Experimental Design 11.1 Introduction 11.2 Experimental Design Terminology 11.3 Controlling the Information in an Experiment 11.4 Noise-Reducing Designs 11.5 Volume-Increasing Designs 11.6 Selecting the Sample Size 11.7 The Importance of Randomization 12. The Analysis of Variance for Designed Experiments 12.1 Introduction 12.2 The Logic Behind an Analysis of Variance 12.3 One-Factor Completely Randomized Designs 12.4 Randomized Block Designs 12.5 Two-Factor Factorial Experiments 12.6 More Complex Factorial Designs (Optional) 12.7 Follow-Up Analysis: Tukey's Multiple Comparisons of Means 12.8 Other Multiple Comparisons Methods (Optional) 12.9 Checking ANOVA Assumptions Case Study 7: Reluctance to Transmit Bad News: The MUM Effect Appendix A: Derivation of the Least Squares Estimates of ß0 and ß1 in Simple Linear Regression Appendix B: The Mechanics of a Multiple Regression Analysis B.1 Introduction B.2 Matrices and Matrix Multiplication B.3 Identity Matrices and Matrix Inversion B.4 Solving Systems of Simultaneous Linear Equations B.5 The Least Squares Equations and Their Solution B.6 Calculating SSE and s2 B.7 Standard Errors of Estimators, Test Statistics, and Confidence Intervals for ß0, ß1, ... , ßk B.8 A Confidence Interval for a Linear Function of the ß Parameters; A Confidence Interval for E(y) B.9 A Prediction Interval for Some Value of y to be Observed in the Future Appendix C: A Procedure for Inverting a Matrix Appendix D: Statistical Tables Table D.1: Normal Curve Areas Table D.2: Critical Values for Student's t Table D.3: Critical Values for the F Statistic: F.10 Table D.4: Critical Values for the F Statistic: F.05 Table D.5: Critical Values for the F Statistic: F.025 Table D.6: Critical Values for the F Statistic: F.01 Table D.7: Random Numbers Table D.8: Critical Values for the Durbin-Watson d Statistic (a =.05) Table D.9: Critical Values for the Durbin-Watson d Statistic (a =.01) Table D.10: Critical Values for the X2-Statistic Table D.11: Percentage Points of the Studentized Range, q(p,v), Upper 5% Table D.12: Percentage Points of the Studentized Range, q(p,v), Upper 1%The Second Course in Statistics is an increasingly important offering since more students are arriving at college having taken AP Statistics in high school. Mendenhall/Sincich s A Second Course in Statistics is the perfect book for courses that build on the knowledge students gain in AP Statistics, or the freshman Introductory Statistics course.A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion.

Anbieter: buecher
Stand: 29.09.2020
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Designing Open Access E-Journals Website
59,00 € *
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Thinking to build a website for open access E-Journals? With this book you ll learn how to design and develop a website with searchable single platform. The methodology adopted is System Analysis and Design (SAD) with web design and scripting. It helps you to put down the basics, design methods, testing, and much more. This book includes System Analysis, Software Development Life Cycle and Designing Open Access E-Journals Website in Computer Science.

Anbieter: Dodax
Stand: 29.09.2020
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A Novel Advancement - Lasers in Conservative De...
61,90 € *
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LASERS have a crucial significance in dental field to overcome some of the disadvantages offered by that of the conventional armamentarium such as drilling sound, anxiety, fear related to conventional handpiece etc. Lasers can be used on both soft and hard tissues and over the time it has proved amenable results. Therefore, modern concepts based on minimal invasive dentistry led to application of Lasers in various fields of dental care. Despite of its Pros and Cons, its advantages makes Laser a ray of hope for the dentistry. This book encompasses the application of Lasers in different aspects of Conservative Dentistry and Endodontics. The text book has simple and easy language and its contents has been taken from various standard journals, textbooks, e-books, website etc.

Anbieter: Dodax
Stand: 29.09.2020
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