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Introduction to Multivariate Analysis
 
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Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 48822 Vidya-mitra
Introduction to Multivariate Data Analysis
 
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Brad Swarbrick, Vice President of Business Development at CAMO Software, gives a shor tintroduction to multivariate data analysis, discusses some of its applications and how these powerful analytical tools are being used to improve products and manufacturing processes in a wide range of industries. Brad Swarbrick is a pharmaceutical industry specialist for CAMO Software with over 20 years experience in the application of chemometrics techniques to spectroscopic analysers and process control systems. He was part of the Pfizer Global Process Analytical Technology (PAT) group in Australia and developed the NIR spectroscopy and PAT programs for Sigma Pharmaceuticals, Australia's largest pharmaceuticals manufacturer. For the past 3 years Brad has been based in Europe, during which time he has helped a number of leading manufacturers realize major process and quality improvements in the pharmaceutical, agricultural, chemical and downstream oil & gas industries across Europe, North America and Asia. Brad has a B.Sc (Hons) in Science and Mathematics, majoring in Chemometrics. He is the Chair of the Community of Practice in PAT, regional board director of the Australian ISPE (International Society for Pharmaceutical Engineering) affiliate, and was a member of the ASTM E55 sub-committee on PAT. Brad has been an invited expert speaker in a wide range of global conferences on PAT, NIR and Quality by Design (QbD), and has authored a number of whitepapers and peer-reviewed journal articles as well as the popular reference book Multivariate Data Analysis for Dummies. In addition to his work at CAMO, Brad has recently taken the role of pharmaceutical editor for the prestigious Journal of NIR Spectroscopy.
Views: 34943 Camo Analytics
Tutorial #1 Introduction to Multivariate Data Analysis
 
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This video is the first in a series of six which cover best practice for analyzing spectra with multivariate data analysis. In this edition we introduce multivariate data analysis, or chemometrics, and why these powerful tools are especially useful for analyzing spectral data. The video gives examples of typical applications, discusses the benefits of Multivariate analysis over Univariate analysis, and gives an explanation of some important multivariate methods. The video concludes with a demonstration of spectral data being analyzed.
Views: 27817 Camo Analytics
Multivariate Statistical Anlaysis in Water Quality
 
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Multivariate statistical techniques are the application of statistics to simultaneous observations and can include the analysis of more than one outcome (dependent) variable. Good multivariate analysis starts with exploratory and graphical analyses to reveal potential relations in the data and to highlight potential outliers. First, this presentation will discuss how to extend univariate and bivariate methods for graphical analysis to multivariate data, as well as methods unique to multivariate data. Second, multivariate outlier detection will be presented. Third, there will be a brief discussion of multivariate statistical analysis methods, such as multiple regression, principal component analysis, and cluster analysis, including examples and suggestions as to when one might want to use these techniques.
Mod-01 Lec-01 Introduction to multivariate statistical modeling
 
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Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 56577 nptelhrd
Univariate Analysis and Bivariate Analysis
 
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Subject: Social Work Education Paper:Research Methods and Statistics Module: Univariate Analysis & Bivariate Analysis Content Writer: Dr. Graciella Tavares
Views: 26326 Vidya-mitra
Multivariate Analysis (HRM)
 
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Subject:Human Resource Management Paper: Research Methodology
Views: 2847 Vidya-mitra
Summary on multivariate exploratory data analysis
 
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What are the important questions that are necessary to answer before performing a principal component method such as Principal Component Analysis, Correspondence Analysis, Multiple Correspondence Analysis, Multiple Factor Analysis ? How to interpret the results of the principal component method ? See the videos on Youtube: http://www.youtube.com/user/HussonFrancois
Views: 489 François Husson
Mod-03 Lec-16 Multivariate Analysis - I
 
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Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit http://nptel.ac.in
Views: 2303 nptelhrd
Univariate, Bivariate and Multivariate analysis EDA Lecture 13@Applied AI Course
 
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for more details visit the following link https://www.appliedaicourse.com/course/applied-ai-course/lessons/summarizing-plots-univariate-bivariate-and-multivariate-analysis-1/
Views: 3725 Applied AI Course
Exploratory Data Analysis 13 Univariate, Bivariate and Multivariate analysis EDA
 
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Follow me on Facebook facebook.com/himanshu.kaushik.2590 Subscribe to our channel on youtube to get latest updates on Video lectures Our video lectures are helpful for examinations like GATE UGC NET ISRO DRDO BARCH OCES DCES DSSSB NIELIT Placement preparations in Computer Science and IES ESE for mechanical and Electronics. Get access to the most comprehensive video lectures call us on 9821876104/02 Or email us at [email protected] Visit Our websites www.gatelectures.com and www.ugcnetlectures.com For classroom coaching of UGC NET Computer Science or GATE Computer Science please call us on 9821876104 Exploratory Data Analysis,Box plot,whiskers,EDA,Machine Learning,Artificial Intelligence,Data science Links of Our Demo lectures playlists Our Courses - https://goo.gl/pCZztL Data Structures - https://goo.gl/HrZE6J Algorithm Design and Analysis - https://goo.gl/hT2JDg Discrete Mathematics - https://goo.gl/QQ8A8D Engineering Mathematics - https://goo.gl/QGzMFv Operating System - https://goo.gl/pzMEb6 Theory of Computation - https://goo.gl/CPBzJZ Compiler Design - https://goo.gl/GhcLJg Quantitative Aptitude - https://goo.gl/dfZ9oD C Programming - https://goo.gl/QRNx54 Computer Networks - https://goo.gl/jYtsCQ Digital Logic - https://goo.gl/3iosMc Database Management System - https://goo.gl/84pCFD Computer Architecture and Organization - https://goo.gl/n9H69F Microprocessor 8085 - https://goo.gl/hz5bvv Artificial Intelligence - https://goo.gl/Y91rk2 Java to Crack OCJP and SCJP Examination - https://goo.gl/QHLKi7 C plus plus Tutorials - https://goo.gl/ex1dLC Linear Programming Problems - https://goo.gl/RnRHXH Computer Graphics - https://goo.gl/KaGsXs UNIX - https://goo.gl/9Le7sX UGC NET November examination video solutions - https://goo.gl/Wos193 NIELIT 2017 Question paper Solutions - https://goo.gl/w9QkaE NIELIT Exam Preparation Videos - https://goo.gl/cXMSyA DSSSB Video Lectures - https://goo.gl/f421JF ISRO 2017 Scientist SC paper Solution - https://goo.gl/bZNssE Computer Graphics - https://goo.gl/uWwtgw Number System Digital logic - https://goo.gl/7Q1vG1 Live Classroom Recordings - https://goo.gl/pB1Hvi Verbal Aptitude - https://goo.gl/oJKwfP Thermodynamics - https://goo.gl/BN5Gd6 Heat and Mass Transfer - https://goo.gl/Lg6DzN Pre and Post GATE Guidance - https://goo.gl/k5Ybnz GATE Preparation Tips by Kishlaya Das GATE AIR 37 - https://goo.gl/jfFWQp #GATE #UGCNET
StatQuest: Principal Component Analysis (PCA), Step-by-Step
 
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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Methodology in multivariate exploratory data analysis
 
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What are the important questions that are necessary to answer before performing a principal component method such as Principal Component Analysis, Correspondence Analysis, Multiple Correspondence Analysis, Multiple Factor Analysis ? How to interpret the results of the principal component method ? See the videos on Youtube: http://www.youtube.com/user/HussonFrancois
Views: 10005 François Husson
Multivariate Statistical Analysis
 
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Animated Video created using Animaker - https://www.animaker.com final
Views: 32 Tender Twilight
BroadE: Statistical methods of data analysis
 
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Copyright Broad Institute, 2013. All rights reserved. The presentation above was filmed during the 2012 Proteomics Workshop, part of the BroadE Workshop series. The Proteomics Workshop provides a working knowledge of what proteomics is and how it can accelerate biologists' and clinicians' research. The focus of the workshop is on the most important technologies and experimental approaches used in modern mass spectrometry (MS)-based proteomics.
Views: 6818 Broad Institute
Factoshiny: interactive graphs in exploratory multivariate data analysis
 
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How to use Factoshiny, the library that allows us to use FactoMineR with a graphical user interface and that allow us to make interactive graphs.
Views: 4803 François Husson
Marketing Statistics in Excel 9.1 Regression Analysis, Univariate and Multivariate Regression
 
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Learn about managing data in Excel. These are the Video supplements for Workbook of Quantitative Tools and Techniques in Marketing, 2nd Ed. Part of a full MOOC.
Views: 3679 Tim J Smith PhD
R Stats: Multiple Regression - Variable Selection
 
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This video gives a quick overview of constructing a multiple regression model using R to estimate vehicles price based on their characteristics. The video focuses on how to employ a method of improving a linear model, and thus its linear equation, by stepwise regression with backward elimination of variables. It will demonstrate the process of building a model by starting with all candidate predictors and eliminating them one by one to optimize the model. The lesson also explains how to guide this optimization process by relying on the measures of model quality, such as R-Squared and Adjusted R-Squared statistics, and how to assess the variables usefulness to the model by judging their p-values, which represent the confidence in their coefficients which are to be used in the linear equation. The final model will be evaluated by calculating the correlation between the predicted and actual vehicle price for both the training and validation data sets. The explanation will be quite informal and will avoid the more complex statistical concepts. Note that a more complex process of building a multiple linear model, with details of variables transformation, checking for their multiple collinearity and extreme values, will be explained in the next lesson. The data for this lesson can be obtained from the well-known UCI Machine Learning archives: * https://archive.ics.uci.edu/ml/datasets/automobile The R source code for this video can be found here (some small discrepancies are possible): * http://visanalytics.org/youtube-rsrc/r-stats/Demo-D1-Multiple-Reg-Var-Selection.r Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 43003 ironfrown
Multivariate methods for "omics" data integration and analysis.
 
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Dr. Alex Sánchez Pla, Statistics and Bioinformatics Unit (UEB), VHIR
boral: R package for multivariate data analysis in Ecology
 
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boral is an R package designed for Bayesian analysis of multivariate data (e.g., community composition data) in ecology. UPDATE June 2016: - There is a software article now on boral, available on the Methods in Ecology and Evolution website: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12514/full - A second video discussing some of the updates to boral that have been made since 2014 can be found here: https://www.youtube.com/watch?v=XmrVVMG1HXI Check it out: http://cran.r-project.org/web/package... Disclaimer: I am in no way affiliated with the construction company in Australia also known as BORAL, although my voice is probably as dry as their cement =P
Views: 5229 Francis Hui
Multivariate techniques: PCA and MDS
 
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Subject:Psychology Paper: Quantitative methods
Views: 120 Vidya-mitra
Univariate Analysis
 
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Let's go on a journey through univariate analysis and learn about descriptive statistics in research!
Views: 44263 ChrisFlipp
Multivariate Statistical Analysis Part 3: MANCOVA (with R Demonstration)
 
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This is the 3rd part of my Multivariate Statistical Analysis video series on Multivariate Analysis of Covariance (MANCOVA). MANCOVA is a method for looking at if the means of the multiple outcome variables between each group (specified by the group variable) are statistically different while controlling for a covariate which can influence the outcome but is not of study interest. Like Me On FB: https://www.facebook.com/RenaissanceMonaLisa/
Views: 434 RenaissanceWoman
Introduction to multivariate analysis
 
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Manufacturers in all industrial sectors continuously face major challenges regarding the improvement of product quality and in the early detection of process deviations. Multivariate Data Analysis (MVA) is well suited for designing quality into processes and provides the essential tools for monitoring Critical to Quality (CTQ) attributes, ensuring acceptable end product quality. The Unscrambler® is a complete Multivariate Analysis and Experimental Design software solution, equipped with powerful methods including PCA, Multivariate Curve Resolution (MCR), PLS Regression, 3-Way PLS Regression, Clustering (K-Means), SIMCA and PLSDA Classification etc.
Views: 45054 Camo Analytics
An Introduction to Multivariate Data Analysis with The Unscrambler X
 
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This webinar will demonstrate The Unscrambler ® for MVA including examples of PCA and PLS regression, with different types of data.
Views: 29530 Camo Analytics
Prof. Brendan Murphy - Model-based clustering for multivariate categorical data
 
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Latent class analysis is the most common model that is used to perform model-based clustering for multivariate categorical responses. The selection of the variables most relevant for clustering is an important task which can affect the quality of clustering considerably. We outline two approaches for model-based clustering and variable selection for multivariate categorical data. The first method uses a Bayesian approach where both clustering and variable selection are carried out simultaneously using an MCMC approach based on a collapsed Gibbs sampler; post-hoc procedures for parameter and uncertainty estimation are outlined. The second method considers a variable selection method based on stepwise model selection using a model that avoids a local independence assumption which is used in competing approaches. The methods are illustrated on a simulated and real data and are shown to give improved clustering performance compared to competing methods. The talk is based on: http://arxiv.org/pdf/1402.6928 and http://arxiv.org/pdf/1512.03350v1.pdf.
Multivariate Data Analysis Overview
 
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Multivariate Data Analysis Overview: Use similarity calculations to find patterns of interest in line charts; Consider the value of applying clustering algorithms to organize multivariate data. This video is current as of Spotfire 6.5. Some interactive features in the original video are not available in YouTube. The original is available here: http://learn.spotfire.tibco.com/mod/url/view.php?id=5361 (Click "Login as guest" to access that link if needed)
Views: 4439 TIBCO Products
Applied Multivariate Statistical Analysis - Class #1
 
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This is a video from Applied Multivariate Statistical Analysis (STAT 873) at the University of Nebraska-Lincoln in fall 2013. See http://www.chrisbilder.com/multivariate (schedule and section materials web pages) for the lecture notes. Students viewed the Introduction to R videos for class #2 - https://youtu.be/dmmI8fj0128.
Views: 6497 Chris Bilder
CFA Level 1: Quantitative Methods - Univariate vs Multivariate Distributions
 
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Watch the next finance lesson: https://bluebookacademy.com/courses
Views: 1451 BlueBookAcademy.com
Applied Multivariate Statistical Analysis
 
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Learn more at: http://www.springer.com/978-3-662-45170-0. Offers a wide scope of methods and applications, making this a comprehensive treatment of the subject. Includes a wealth of examples and exercises—ideal for students in economics and finance. Quantlets in R and Matlab available online.
Views: 137 SpringerVideos
A Ph.D. Workshop on Multivariate Research Methods
 
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In this introductory doctoral workshop, a review of advanced multivariate research methods in support of Information Systems (IS) research is provided. Topics we will cover: • Understand the assumptions and limitations of multivariate research • Conduct multivariate data collection • Organizing multivariate data for analyses • Understand the need for and able to conduct pre-analysis data preparation • Understand the need for and able to conduct multivariate reliability testing (Cronbach's Alpha) • Overview of the different multivariate data analyses Tools we will be using include Statistical Package for the Social Sciences (SPSS, an IBM company) SPSS Statistics -- For a reduced rate for students: http://www.onthehub.com/spss/
Views: 6928 YairLevyPhD
Multivariate Outlier Analysis/Detection Using Scatter plots and boxplots in Python - Tutorial 21
 
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In this Video Tutorial on Python for Data science, you will learn about multivariate outlier detection using Scatter plots and box plots in python. programming environment used for coding is Jupyter notebook.
Views: 3115 TheEngineeringWorld
Learn Cluster Analysis | Cluster Analysis Tutorial | Introduction to Cluster Analysis
 
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#ClusterAnalysis | A tutorial on Cluster Analysis using real-life examples. Learn the objective of cluster analysis, the methodology used and interpreting results from the same. Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Clustering methods can be classified into the following categories − - Partitioning Method - Hierarchical Clustering - Density-based Method - Grid-Based Method - Model-Based Method - Constraint-based Method Learn More at: https://goo.gl/DszCyk Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #WhatIsClusterAnalysis #Tutorial #GreatLearning #GreatLakes About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/
Views: 29402 Great Learning
Introduction to Spectral data analysis
 
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This webinar shows how to use The Unscrambler® for analysing spectra. It addresses people dealing with or having preliminary knowledge of spectral data. It uses a case study that can help understanding these methods. During this webinar, you will see how to: 1) Perform pre-treatments of the spectra 2) Analyse spectral data using first an exploratory data analysis method (PCA) and then a classification method (SIMCA)
Views: 17949 Camo Analytics
Mod-03 Lec-17 Multivariate Analysis - II
 
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Statistical Methods for Scientists and Engineers by Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur For more details on NPTEL visit http://nptel.ac.in
Views: 589 nptelhrd
MANOVA - SPSS (part 1)
 
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I perform and interpret a MANOVA in SPSS in two ways. First, the more common method using the GUI. Then, the more sophisticated and insightful way through syntax which allows for an understanding of the discriminant function that is created by the MANOVA procedure. Anyone who makes it to the end of this video series is a champion. Learn how to report MANOVA results: http://how2stats.blogspot.com/2011/10/manova-reporting-type-1.html http://how2stats.blogspot.com/2011/10/manova-reporting-type-2.html SPSS Multivariate Analysis of Variance syntax: MANOVA dependent1 dependent 2 dependent3 by independent (1, 3) /DISCRIM=STAN RAW CORR /PRINT=SIGNIF(MULTIV, UNIV, EIGEN, DIMENR) /DESIGN.
Views: 260572 how2stats
Exploring Multivariate Event Sequences using Rules, Aggregations, and Selections
 
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Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the temporal analysis of a single attribute or the structural analysis of the multivariate data only. We present an approach where users can explore event sequences at multivariate and sequential level simultaneously by interactively defining a set of rewrite rules using multidimensional regular expressions. Users can store resulting patterns as new types of events or attributes to interactively enrich or simplify event sequences for further investigation. In \system{} we provide a bottom-up glyph-oriented approach for multivariate event sequence analysis by searching, clustering, and aligning them according to newly defined domain specific properties. We illustrate the effectiveness of our approach with real-world data sets including telecommunication traffic and hospital treatments.
Views: 438 Bram Cappers
Applied Multivariate Statistical Analysis - Class #4
 
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This is a video from Applied Multivariate Statistical Analysis (STAT 873) at the University of Nebraska-Lincoln in fall 2013. See http://www.chrisbilder.com/multivariate (schedule and section materials web pages) for the lecture notes.
Views: 1242 Chris Bilder
Multivariate Statistical Methods: Advanced Topics
 
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Robert A. Henson, Associate Professor in the School of Education at the University of North Carolina at Greensboro, describes his ICPSR Summer Program workshop "Multivariate Statistical Methods: Advanced Topics." For more information about this workshop, visit http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0009 For more information about the ICPSR Summer Program, visit icpsr.umich.edu/sumprog
Multivariate Network Exploration and Presentation
 
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Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.
Views: 181 ieeeComputerSociety

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