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Specific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data setsĢ Review of Regression and More About R 8Ģ.2.3 Matrix Representation of the Problem, 9Ģ.3 Simulating the Data from a Model and Estimating the Model Parameters in R, 9Ģ.3.2 Estimating the Model Parameters in R, 9Ģ.5 Residuals Analysis-What Can Go Wrong…, 13ģ The Modeling Approach Taken in this Book and Some Examples of Typical Serially Correlated Data 18ģ.3 Simple Regression in the Framework, 20ģ.5 The Diversity of Time Series Data, 21ģ.6.2 The Diskette and the scan() and ts() Functions-New York City Temperatures, 25ģ.6.3 The Diskette and the read.table() Function-The Semmelweis Data, 25ģ.6.4 Cut and Paste Data to a Text Editor, 26Ĥ.5 Power of Logarithmic Transformations Illustrated, 32ĥ The Autocorrelation Function And AR(1), AR(2) Models 35ĥ.1 Standard Models-What are the Alternatives to White Noise?, 35ĥ.2 Autocovariance and Autocorrelation, 36ĥ.2.5 Estimation of the Autocovariance and Autocorrelation, 37ĥ.3.2 The Basic Code for Estimating the Autocovariance, 38ĥ.4 The First Alternative to White Noise: Autoregressive Errors-AR(1), AR(2), 40ĥ.4.1 Definition of the AR(1) and AR(2) Models, 40ĥ.4.3 The AR(1) Model Autocorrelation and Autocovariance, 41ĥ.4.4 Using Correlation and Scatterplots to Illustrate the AR(1) Model, 41ĥ.4.5 The AR(2) Model Autocorrelation and Autocovariance, 41ĥ.4.6 Simulating Data for AR(m) Models, 42ĥ.4.7 Examples of Stable and Unstable AR(1) Models, 44ĥ.4.8 Examples of Stable and Unstable AR(2) Models, 46Ħ The Moving Average Models MA(1) And MA(2) 51Ħ.2 The Autocorrelation for MA(1) Models, 51Ħ.3 A Duality Between MA(l) And AR(m) Models, 52Ħ.4 The Autocorrelation for MA(2) Models, 52Ħ.5 Simulated Examples of the MA(1) Model, 52Ħ.6 Simulated Examples of the MA(2) Model, 54Ħ.7 AR(m) and MA(l) model acf() Plots, 54.Numerous exercise sets intended to support readers understanding of the core concepts.
Basic data analysis software#
Multiple R software subroutines employed with graphical displays.Real-world examples to provide readers with practical hands-on experience.In addition, Basic Data Analysis for Time Series with R also features:
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The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals.įocusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC).
Basic data analysis serial#
Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space.