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Learn exploratory data analysis concepts using powerful R packages to enhance your R data analysis skills Key Features Speed up your;
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some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use;
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This volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The reader will find numerous;
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...provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks;
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"An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal;
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fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA;
Vergelijkbare producten zoals Exploratory Multivariate Analysis by Example Using R
fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA;
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analysis points, multi-dimensional data analysis, and analysis of remote sensing data, cancer progression, chaos theory, epidemiological modeling;
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solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data;
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A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. Exploratory;
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fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different;
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> 0134995457 / 9780134995458 EXPLORATORY DATA ANALYSIS (CLASSIC VERSION), 1/e ;
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contexts is whether the structure of associations that underlies the data is accounted for by a latent construct. Exploratory factor analysis is a;
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. This textbook, Exploratory Data Analysis in Business and Economics, aims to familiarise students of economics and business as well as;
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Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should;
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Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are;
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described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and;
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described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and;
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based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to;
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Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that;
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edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and;
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and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal;
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Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain;
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objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.
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developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis;
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The approach in this introductory book is that of informal study of the data. Methods range from plotting picture;
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This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open;
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