This book introduces various widely available exploratory data analysis methods, emphasizing those that are most useful in the preliminary exploration of large datasets involving mixed data types. Topics include descriptive statistics, graphical analysis tools, regression modeling and spectrum estimation, along with practical issues like outliers, missing data, and variable selection.
This book introduces various widely available exploratory data analysis methods, emphasizing those that are most useful in the preliminary exploration of large datasets involving mixed data types. Topics include descriptive statistics, graphical analysis tools, regression modeling and spectrum estimation, along with practical issues like outliers, missing data, and variable selection.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ronald Pearson has held a wide variety of technical positions in both academia and industry, including the DuPont Company, the Swiss Federal Institute of Technology (ETH, Zurich), the Tampere University of Technology in Tampere, Finland, and most recently, the Travelers Companies. Dr. Pearson's experience has included the analysis and modeling of industrial process operating data, the design of nonlinear digital filters for data cleaning applications, the analysis of historical clinical data, and he is currently involved in developing models for predictive analytics applied to large business datasets. His research interests include model structure selection for nonlinear discrete-time dynamic models of empirical data, the algebraic characterization and design of nonlinear digital filters, and the development of exploratory data analysis techniques for large datasets involving mixed data types.
Inhaltsangabe
1.: The Art of Analyzing Data 2.: Data: Types, Uncertainty and Quality 3.: Characterizing Categorical Variables 4.: Uncertainty in Real Variables 5.: Fitting Straight Lines 6.: A Brief Introduction to Estimation Theory 7.: Outliers: Distributional Monsters That Lurk in Data 8.: Characterizing a Dataset 9.: Confidence Intervals and Hypothesis Testing 10.: Associations between Variables 11.: Regression Models I: Real Data 12.: Re-expression: Data Transformations 13.: Regression Models II: Mixed Data Types 14.: Characterizing Analysis Results 15.: Regression Models III: Diagnostics and Refinements 16.: Dynamic Data Characterization 17.: Linear Data Filters 18.: Nonparametric Spectrum Estimation 19.: Irregularities in Dynamic Analysis 20.: Dealing with Missing Data
1.: The Art of Analyzing Data 2.: Data: Types, Uncertainty and Quality 3.: Characterizing Categorical Variables 4.: Uncertainty in Real Variables 5.: Fitting Straight Lines 6.: A Brief Introduction to Estimation Theory 7.: Outliers: Distributional Monsters That Lurk in Data 8.: Characterizing a Dataset 9.: Confidence Intervals and Hypothesis Testing 10.: Associations between Variables 11.: Regression Models I: Real Data 12.: Re-expression: Data Transformations 13.: Regression Models II: Mixed Data Types 14.: Characterizing Analysis Results 15.: Regression Models III: Diagnostics and Refinements 16.: Dynamic Data Characterization 17.: Linear Data Filters 18.: Nonparametric Spectrum Estimation 19.: Irregularities in Dynamic Analysis 20.: Dealing with Missing Data
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