MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Linear Algebra introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to work in linear algebra with ease. In addition to exploring MATLAB’s matrix algebra capabilities, it describes the MATLAB commands that are used to create two- and three-dimensional graphics, including explicit, implicit and parametric curve and surface plotting, and various methods of data representation. Methods for solving systems of equations are detailed.
En este libro se desarrollarán técnicas de aprendizaje supervisado relativas a regresión. Más concretamente, se profundizará en los modelos lineales de regresión múltiple con toda su problemática de identificación, estimación y diagnosis. Se hace especial hincapié en el tratamiento de la multicolinealidad a través de la Ridge Regression (regresión en cadena) y el método PLS de los mínimos cuadrados parciales. Se dedica una parcela importante del contenido a los modelos de variable dependiente limitada y recuento, con especial mención a los modelos Logit y Probit. Por último se tratan también los modelos predictivos del análisis de la varianza y la covarianza.
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks. This book deeps in big data and deep learning techniques
Usually explanatory variables in an econometric model are supposed related at one time with the endogenous variable, so usually the temporary sub-indices of all variables are equal. However, economic theory and other sciences lead us to dynamic relationship between the variables, since the impacts between variables can become manifest in later periods or extended to many periods. In this way appear dynamic models with variables out in time. Dynamic models usually seen three different situations according to the variables affected by delays. It may be that the delays involved only to exogenous variables, only the endogenous variable or simultaneously to endogenous and exogenous variables. This book covers a wide typology of dynamic models including models with distributed delays, models with stochastic regressors, models with structural change and dynamic panel data models. Widely is the theory of unit roots, the Cointegration and error correction models. And all this from a perspective multi-software, using the latest software on the market suitable for these non-trivial econometric tasks (SAS, EVIEWS, SPSS and STATA).
Usually explanatory variables in an econometric model are supposed related at one time with the endogenous variable, so usually the temporary sub-indices of all variables are equal. However, economic theory and other sciences lead us to dynamic relationship between the variables, since the impacts between variables can become manifest in later periods or extended to many periods. In this way appear dynamic models with variables out in time. Dynamic models usually seen three different situations according to the variables affected by delays. It may be that the delays involved only to exogenous variables, only the endogenous variable or simultaneously to endogenous and exogenous variables. This book covers a wide typology of dynamic models including models with distributed delays, models with stochastic regressors, models with structural change and dynamic panel data models. Widely is the theory of unit roots, the Cointegration and error correction models. And all this from a perspective multi-software, using the latest software on the market suitable for these non-trivial econometric tasks (SAS, EVIEWS, SPSS and STATA).
R is a programming language and environment for statistical and graphical analysis. It is a free software project widely used by the statistical community. R implements a multitude of commands and functions to work in the statistical field. Specifically, this book delves into the work with R in Descriptive Statistics. It incorporates the functions for initial data treatment, exploratory data analysis, statistical graphs, quantitative expression of distributions, measures of centralization, measures of dispersion and shape, correlation, covariance, linear regression model, logistic regression, probit model, and other topics of descriptive statistics. The chapters begin with brief methodological notes and incorporate a variety of examples and exercises solved with the R software.
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