Statistics in MATLAB

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A01=MoonJung Cho
A01=Wendy L. Martinez
advanced MATLAB statistical techniques
Agglomerative Clustering
applied statistics
Author_MoonJung Cho
Author_Wendy L. Martinez
Base MATLAB
biostatistical methods
Category=UFM
CDF
Class Conditional Probabilities
Classical MDS
Classification
Clustering
Data Set
Descriptive Statistics
economic data analysis
engineering data modeling
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eq_isMigrated=2
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Fisher's Iris Data
Generate Random Variables
Hypothesis Testing
Interpoint Distances
Iris Data
MATLAB Basics
MATLAB Capability
MATLAB Functionality
MATLAB Introduction
MATLAB Primer
MATLAB Statistics Toolbox
Model-Building
Multivariate Analysis
Naive Bayes Classifier
Nonmetric MDS
Normal Reference Rule
Parallel Coordinate
Parallel Coordinate Plot
PCA Function
Plotting Tools
Probability Distributions
Quadratic Classifier
quantitative data analysis
Regression Analysis
Resubstitution Error
Scatter Plot
Scatter Plot Matrix
Sepal Length
Silhouette Plot
statistical computing
Statistics Toolbox
Visualizing Data

Product details

  • ISBN 9781466596566
  • Weight: 530g
  • Dimensions: 138 x 216mm
  • Publication Date: 15 Dec 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
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Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB® and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book:

  • Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB
  • Presents examples of how MATLAB can be used to analyze data
  • Offers access to a companion website with data sets and additional examples
  • Contains figures and visual aids to assist in application of the software
  • Explains how to determine what method should be used for analysis

Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.

Wendy L. Martinez is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has co-authored two additional successful Chapman Hall/CRC books on MATLAB and statistics, and has been using MATLAB for more than 15 years to solve problems and conduct research in statistics and engineering.

MoonJung Cho is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has more than10 years of experience in survey methodology research and applications, and is knowledgeable of other software packages, such as SAS and R. She is able to use this knowledge to enhance the utility of this book to users of other statistical software packages.

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