Catálogo de publicaciones - libros
The Definitive Guide to MySQL5
Michael Kofler
Third Edition.
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Software Engineering/Programming and Operating Systems
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-1-59059-535-0
ISBN electrónico
978-1-4302-0071-0
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Apress 2005
Cobertura temática
Tabla de contenidos
What Is MySQL?
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 1 - Introduction | Pp. 3-16
The Test Environment
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 1 - Introduction | Pp. 17-45
Introductory Example (An Opinion Poll with PHP)
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 1 - Introduction | Pp. 47-58
mysql, mysqladmin, and mysqldump
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 2 - Administrative Tools and User Interfaces | Pp. 61-70
MySQL Administrator and MySQL Query Browser
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 2 - Administrative Tools and User Interfaces | Pp. 71-85
phpMyAdmin
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 2 - Administrative Tools and User Interfaces | Pp. 87-116
Microsoft Office, OpenOffice/StarOffice
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 2 - Administrative Tools and User Interfaces | Pp. 117-134
Database Design
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 3 - Fundamentals | Pp. 137-187
An Introduction to SQL
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 3 - Fundamentals | Pp. 189-216
SQL Recipes
Michael Kofler
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
Part 3 - Fundamentals | Pp. 217-261