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INTRODUZIONE ALL'ANALISI E PREVISIONE DELLE SERIE TEMPORALI di Douglas C. Montgomery-
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Numero oggetto eBay:226235560109
Specifiche dell'oggetto
- Condizione
- Come Nuovo
- Note del venditore
- ISBN-10
- 0471653977
- Book Title
- Introduction to Time Series Analysis and Forecasting
- ISBN
- 9780471653974
- Subject Area
- Mathematics, Social Science
- Publication Name
- Introduction to Time Series Analysis and Forecasting
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.6 in
- Subject
- Future Studies, Probability & Statistics / General, Probability & Statistics / Time Series
- Publication Year
- 2008
- Series
- Wiley Series in Probability and Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.1 in
- Item Weight
- 28.4 Oz
- Item Width
- 6.3 in
- Number of Pages
- 472 Pages
Informazioni su questo prodotto
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471653977
ISBN-13
9780471653974
eBay Product ID (ePID)
60701321
Product Key Features
Number of Pages
472 Pages
Language
English
Publication Name
Introduction to Time Series Analysis and Forecasting
Publication Year
2008
Subject
Future Studies, Probability & Statistics / General, Probability & Statistics / Time Series
Type
Textbook
Subject Area
Mathematics, Social Science
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
28.4 Oz
Item Length
9.6 in
Item Width
6.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2007-019891
Dewey Edition
23
Reviews
"This would be an appropriate source for use in a first course in time series analysis. It might also be useful as a reference for researchers who want to apply time series analysis to their data sets." ( CHOICE , October 2008) "The result is a book that can be used with a wide variety of audiences, with different interests and technical backgrounds, whose common interests are understanding how to analyze time-oriented data and constructing good short-term statistically based forecasts." ( Mathematical Reviews , 2008m) "The book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." ( MAA Reviews, July 2008), "This would be an appropriate source for use in a first course in time series analysis. It might also be useful as a reference for researchers who want to apply time series analysis to their data sets." ( CHOICE Oct 2008) "The result is a book that can be used with a wide variety of audiences, with different interests and technical backgrounds, whose common interests are understanding how to analyze time-oriented data and constructing good short-term statistically based forecasts." ( Mathematical Reviews Aug 2008) "The book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." ( MAA Reviews, July 2008)
Series Volume Number
526
Illustrated
Yes
Dewey Decimal
519.5/5
Synopsis
An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: * Regression-based methods, heuristic smoothing methods, and general time series models * Basic statistical tools used in analyzing time series data * Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performanceover time * Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares * Exponential smoothing techniques for time series with polynomial components and seasonal data * Forecasting and prediction interval construction with a discussion ontransfer function models as well as intervention modeling and analysis * Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab(r), JMP(r), and SAS(r) software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint(r) slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series coursesat the advanced undergraduate and beginning graduate levels. The book also serves as an indispensablereference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences., An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performanceover time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series coursesat the advanced undergraduate and beginning graduate levels. The book also serves as an indispensablereference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences., An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences., Introduction to Time Series Analysis and Forecasting examines methods for modeling and analyzing time series data with a view towards drawing inferences about the data and generating forecasts that will be useful to the decision maker.
LC Classification Number
QA280.M662 2007
Copyright Date
2008
ebay_catalog_id
4
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