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Robustness in Econometrics

This book presents recent research on robustness in econometrics. Robust data processing techniques - i.e., techniques that yield results;

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Applied Statistical Designs for the Researcher

counterparts according to the strength of the collected data. Applied Statistical Designs for the Researcher discusses three varieties of the Student t;

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Applied Statistical Designs for the Researcher

counterparts according to the strength of the collected data. Applied Statistical Designs for the Researcher discusses three varieties of the Student t;

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Fundamental Statistical Neurobiologist

types of data are discussed such as how to formulate a research hypothesis, the primary types of statistical errors and statistical power;

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Applied Compositional Data Analysis

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and;

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How to Manage, Analyze, and Interpret Survey Data

to manage survey data and become better users and consumers of statistical and qualitative survey information. Fink explains the basic;

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Understanding and Applying Basic Statistical Methods Using R

Statistical Methods Using R also includes: * Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple;

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Statistical Thinking

: A new chapter on data, assessing data pedigree (quality), and acquisition tools Discussion of the relationship between statistical thinking;

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Outliers in Statistical Data

From its initial publication this book has been the standard text on the subject. Since then there h;...

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Handbook of Univariate and Multivariate Data Analysis with IBM SPSS

, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets;

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Handbook of Univariate and Multivariate Data Analysis with IBM SPSS

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis;

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Robust Multivariate Analysis

published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics;

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Data Analysis

broader spectrum of questions It reduces the number of statistical techniques that must be memorized It teaches readers how to become data;

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Visualize This

Practical data design tips from a data visualization expert of the modern age Data doesn?t decrease; it is ever-increasing and can be;

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Mixed Effects Models for Complex Data

studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate;

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Mixed Effects Models for Complex Data

studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate;

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Statistical Methods for Imbalanced Data in Ecological and Biological Studies

data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems;

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R Statistics Cookbook

statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then;

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Outlier Detection for Temporal Data

statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier;

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Regression Analysis in Medical Research

methods. In the past few years, professionals have been flooded with big data. The Covid-19 pandemic gave cause for statistical software;

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Regression Analysis in Medical Research

methods. In the past few years, professionals have been flooded with big data. The Covid-19 pandemic gave cause for statistical software;

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A Primer in Biological Data Analysis and Visualization Using R

R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;

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A Primer in Biological Data Analysis and Visualization Using R

R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data;

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Multilevel Modelling of Health Statistics

framework. Important statistical concepts, such as sampling and outliers, are covered specifically for multilevel data. Repeated measures, outliers;

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Robust Methods in Biostatistics

to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not;

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Data Science for Supply Chain Forecasting

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain;

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Model-Based Clustering and Classification for Data Science

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many;

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