Basic Data Analysis for Time Series with R 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 frequenc This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level but with the necessary theory in mind the author uses frequency and time domain and trigonometric regression as themes throughout the book This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level but with the necessary theory in mind the author uses frequency and time domain and trigonometric regression as themes throughout the book. The content includes modern topics such as wavelets Fourier series and Akaikes Information Criterion (AIC) which is not typical of current day classics. Applications to a variety of scientific fields are showcased. Exercise sets are well crafted with the express intent of supporting pedagogy through recognition and repetition. R subroutines are employed as the software and graphics tool of choice. Brevity is a key component to the retention of the subject matter. The book presumes knowledge of linear algebra probability data analysis and basic computer programming