SAS Visual Statistics is an add-on for SAS Visual Analytics that allows you to develop and test models using the in-memory capabilities of SAS LASR Analytic Server. SAS Analytics Visual Explorer (the explorer) allows you to explore, investigate, and visualize big data sources to discover relevant patterns. SAS Visual Statistics extends these capabilities to create, test, and compare models based on the patterns discovered in the explorer. SAS Visual Statistics can export the programming code, before or after performing the model comparison, for use with other SAS products and to put the model into production. SAS High-performance Analytics uses large volumes of data to perform data mining tasks such as pattern discovery, prediction and other typical mining tasks. It allows predictive models to be developed from whole data, not just subsets, so accurate insights can be obtained in minutes or seconds. The use of sophisticated analytics, the ability to work with large numbers of variables, frequent modelling iterations and other advanced features provide competitive advantages. With SAS In-Memory Statistics for Hadoop, is possible to work with rapid interactive analysis, statistical algorithms and machine learning techniques, random decision forests, descriptive statistics, analytical data preparation and Interactive in-memory programming.

BIG DATA TOOLS. SAS VISUAL STATISTICS, SAS HIGH PERFORMANCE ANALYTICS AND SAS IN-MEMORY STATISTICS

Computers & Technology

PÉREZ

Computer Science

Scientific Books

This book explores the panel data econometrics through STATA. The most important topics are the following: Linear regression estimators in panel data models, fixed and random effects, heteroskedasticity and autocorrelation in panel data models, instrumental variables and two stage least squares in panel data models, dynamic panel data models, logit and probit panel data models, censored panel data models, count panel data models, Tobit panel data models, Poisson panel data models, negative binomial panel data models and others models with panel data.

ECONOMETRIC MODELS with PANEL DATA. APPLICATIONS with STATA

The objective of this book is to present the work with MATLAB and especially their applications in the field of Mathematical Analysis and the Infinitesimal Calculus. The book begins by introducing the reader to the use of numbers, operators, variables and functions. The following topics delve into working with 2D and 3D graphics. The following part covers limits, functions, continuity and numerical and power series. Then differentiation is addressed in one and several variables including differential theorems for vector fields. Thereafter the topic of integration is handled including improper integrals, definite and indefinite integration, integration in multiple variables and multiple integrals and their applications. Differential equations are exemplified in detail, Laplace transforms, Tayor series, and the Runga-Kutta method and partial differential equations.

MATHEMATICAL ANALYSIS with MATLAB

The objective of this book is to present the work with MAPLE and especially their applications in the field of Mathematical Analysis and the Infinitesimal Calculus. The book begins by introducing the reader to the use of numbers, operators, variables and functions. The following part covers limits, functions, continuity and numerical and power series. Then differentiation is addressed in one and several variables including differential theorems for vector fields. Thereafter the topic of integration is handled including improper integrals, definite and indefinite integration, integration in multiple variables and multiple integrals and their applications. Differential equations are exemplified in detail

Scientific Calculus with MAPLE. Mathematical Analysis

MACHINE LEARNING WITH MATLAB. SUPERVISED LEARNING AND CLASSIFICATION

ADVANCED TECHNIQUES FOR MULTIVARIATE DATA ANALYSIS USING PYTHON. PREDICTIVE MODELS FOR CLASSIFICATION AND SEGMENTATION

CONTROL DE CALIDAD. Metodología y aplicaciones. Ejercicios resueltos con EXCEL (Spanish Edition)

MACHINE LEARNING WITH MATLAB. UNSUPERSIDED LEARNING AND CLASSIFICATION

DEEP LEARNING WITH MATLAB. NEURAL NETWORKS ARCHITECTURES

MATHEMATICA BY EXAMPLE. Programming Language Elements

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