A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise.
A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise.
A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise: Is there a short hand procedure for a statistical test available in SAS or R?
A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise: Is there a short hand procedure for a statistical test available in SAS or R?
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools.
Presents modern methods to analyzing data with multiple applications in a variety of scientific fields 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.
Presents modern methods to analyzing data with multiple applications in a variety of scientific fields 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.
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools.
A how to guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation.
A how to guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation.
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets.
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets.
Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS Performing Data Analysis Using IBM SPSS uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets.
Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS Performing Data Analysis Using IBM SPSS uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets.
Starting from a basic knowledge of mathematics and mechanics gained in standard foundation classes, Theory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave takes the reader conceptually through from the fundamental mechanics of lift to the stage of actually being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and planform geometries.
Starting from a basic knowledge of mathematics and mechanics gained in standard foundation classes, Theory of Lift: Introductory Computational Aerodynamics in MATLAB/Octave takes the reader conceptually through from the fundamental mechanics of lift to the stage of actually being able to make practical calculations and predictions of the coefficient of lift for realistic wing profile and planform geometries.
Problem Solving and Data Analysis Using Minitab A clear and easy guide to six Sigma methodology Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving.
Problem Solving and Data Analysis Using Minitab A clear and easy guide to six Sigma methodology Six Sigma statistical methodology using Minitab Problem Solving and Data Analysis using Minitab presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving.
Introduces the graphical capabilities of R to readers new to the software Due to its flexibility and availability, R has become the computing software of choice for statistical computing and generating graphics across various fields of research.
Introduces the graphical capabilities of R to readers new to the software Due to its flexibility and availability, R has become the computing software of choice for statistical computing and generating graphics across various fields of research.
Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields.
Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields.
The updated guide to the newest graphing calculator from Texas Instruments The TI-Nspire graphing calculator is popular among high school and college students as a valuable tool for calculus, AP calculus, and college-level algebra courses.
Geographic information systems (GIS) is a rapidly advancing field that has become thoroughly integrated into our society and inseparable from our daily lives.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
