The Resource Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource)
Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource)
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The item Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriKansas City Libraries.This item is available to borrow from 3 library branches.
Resource Information
The item Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of MissouriKansas City Libraries.
This item is available to borrow from 3 library branches.
 Summary

 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Annotation
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Annotation:
 Language
 eng
 Edition
 First edition.
 Extent
 1 online resource (xiii, 98 pages)
 Note
 Part of: 2014 digital library
 Contents

 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 Isbn
 9781606499757
 Label
 Building better econometric models using cross section and panel data
 Title
 Building better econometric models using cross section and panel data
 Statement of responsibility
 Jeffrey A. Edwards
 Language
 eng
 Summary

 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Annotation
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results
 Annotation:
 Cataloging source
 CaBNVSL
 http://library.link/vocab/creatorName
 Edwards, Jeffrey A
 Dewey number
 330.015195
 LC call number
 HB141
 LC item number
 .E383 2014
 Series statement
 Economics collection,
 http://library.link/vocab/subjectName
 Econometric models
 Summary expansion

 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finelytuned empirical model that yields better results
 Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employa methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finelytuned empirical model that yields better results
 Label
 Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource)
 Note
 Part of: 2014 digital library
 Bibliography note
 Includes bibliographical references (pages 9596) and index
 Contents

 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 Control code
 OCM1bookssib017593101
 Dimensions
 unknown
 Edition
 First edition.
 Extent
 1 online resource (xiii, 98 pages)
 Governing access note
 License restrictions may limit access
 Isbn
 9781606499757
 Specific material designation
 remote
 System control number
 (WaSeSS)ssib017593101
 System details

 Mode of access: World Wide Web
 System requirements: Adobe Acrobat reader
 Label
 Building better econometric models using cross section and panel data, Jeffrey A. Edwards, (electronic resource)
 Note
 Part of: 2014 digital library
 Bibliography note
 Includes bibliographical references (pages 9596) and index
 Contents

 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 1. What is a statistically adequate model and why is it important?  2. Basic misspecifications  3. Misspecifications for the more advanced reader  4. Original specification and drawing inference from it: two related models  5. Basic misspecification testing and respecification: the crosssectional case  6. Variance heterogeneity: the crosssectional case  7. Basic misspecification testing and respecification: the panel data case  8. Variance heterogeneity: the panel data case  9. Consistent and balanced panels  10. Dynamic parametric heterogeneity  Conclusion  References  Index
 Control code
 OCM1bookssib017593101
 Dimensions
 unknown
 Edition
 First edition.
 Extent
 1 online resource (xiii, 98 pages)
 Governing access note
 License restrictions may limit access
 Isbn
 9781606499757
 Specific material designation
 remote
 System control number
 (WaSeSS)ssib017593101
 System details

 Mode of access: World Wide Web
 System requirements: Adobe Acrobat reader
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