La estadística es una materia en alza, en una época en la que los datos son un activo muy importante y el análisis cuantitativo tiene aplicaciones muy relevantes en diferentes campos de la ciencia como la economía, las finanzas, la ingeniería, la matemática en general, la sociología, la psicología, la biología, la medicina, etc. Es clave para empezar a trabajar en estadística, sea cual sea el campo en que se aplique, la comprensión de los propios conceptos estadísticos, para posteriormente realizar la operatoria matemática necesaria para desarrollarlos a través del software, que facilita obtener los resultados de los procedimientos estadísticos de forma rápida y con menos errores que cuando las operaciones se realizaban manualmente. Este libro trata los temas elementales de la estadística desde una óptica aplicada, pero a la vez formal. Se presentan coincenzudamente los conceptos, intentando que sean inteligibles para lectores con formación diversa (ciencias, letras, etc.) Las técnicas estadísticas se describen en lenguaje asequible, y se elimina el problema de la operatoria tediosa matemática utilizando un programa tan sencillo y disponible para todos como es Excel. Los capítulos se complementan con ejercicios resueltos totalmente, tanto con su operatoria matemática habitual, como con el apoyo del software, con la finalidad de facilitar al lector la comprensión de los conceptos.

EXCEL Y LA ESTADÍSTICA (Spanish Edition)

Computers & Technology

Pérez López, César

Software

Independently published

MACHINE LEARNING WITH MATLAB. UNSUPERVISED LEARNING TECHNIQUES: CLASSIFICATION

lulu.com

Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Classification models predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Typical applications include medical research, fraud detection, and credit scoring. This book develops classification predictive techniques: Naive Bayes, Nearest Neighbors and Neural Networks. Exercises are solved with MATLAB software.

DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS and NEURAL NETWORKS: Examples with MATLAB

Computer Science

Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Classification models predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Typical applications include medical research, fraud detection, and credit scoring. This book develops the most important classification predictive techniques: Logistic regression, discriminant analysis, decision trees and classification support vector machine. Exercises are solved with MATLAB software.

DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES: Examples with MATLAB

Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Predictive techniques uses regression techniques to develop predictive models. This book develoop ensemble methods, boosting, bagging, random forest, decision trees and regression trees. Exercises are solved with MATLAB software.

DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES: ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB

DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES: REGRESSION, GENERALIZED LINEAR MODELS, SUPPORT VECTOR MACHINE and NEURAL NETWORKS: Examples with MATLAB

DATA MINING and MACHINE LEARNING: CLUSTER ANALYSIS and kNN CLASSIFIERS. Examples with MATLAB

Análisis multivariante de datos: Aplicaciones con IBM SPSS, SAS y STATGRAPHICS

Science & Math

Mathematics

Ibergarceta Publicaciones S.L.

Técnicas de analisís multivariante de datos: Aplicaciones con SPSS (Spanish Edition)

Alhambra

Técnicas de muestreo estadístico

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