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. 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. The book illustrates these principles of model
selection and model building through the use of information
criteria, cross validation, hypothesis tests, and confidence
intervals.
Focusing 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). In addition, "Basic Data Analysis for Time Series
with R" also features:
Real-world examples to provide readers with practical hands-on
experienceMultiple R software subroutines employed with graphical
displaysNumerous exercise sets intended to support readers
understanding of the core conceptsSpecific chapters devoted to the
analysis of the Wolf sunspot number data and the Vostok ice core
data sets
General
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