In statistics, factor analysis of mixed data or factorial analysis of mixed data (FAMD, in the French original: AFDM or Analyse Factorielle de Données Mixtes), is the factorial method devoted to data tables in which a group of individuals is described both by quantitative and qualitative variables. It belongs to the exploratory methods developed by the French school called Analyse des données (data analysis) founded by Jean-Paul Benzécri. The term mixed refers to the use of both quantitative and qualitative variables. Roughly, we can say that FAMD works as a principal components analysis (PCA) for quantitative variables and as a multiple correspondence analysis (MCA) for qualitative variables. (Wikipedia).
How to find correlation in Excel with the Data Analysis Toolpak
Click this link for more information on correlation coefficients plus more FREE Excel videos and tips: http://www.statisticshowto.com/what-is-the-pearson-correlation-coefficient/
From playlist Regression Analysis
Correlation Coefficient (2 of 2: Evaluating with a calculator)
More resources available at www.misterwootube.com
From playlist Bivariate Data Analysis
StatGeoChem session 5 Factor Analysis
PCA and Factor analysis with applications in Geosciences
From playlist Statistical Geochemistry
An introduction to Regression Analysis
Regression Analysis, R squared, statistics class, GCSE Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Linear Regression http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C Using SPSS for Multiple Linear Regression http://www.youtube.com/playlist?li
From playlist Linear Regression.
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representing multivariate random signals using principal components. Principal component analysis identifies the basis vectors that describe the la
From playlist Random Signal Characterization
Estimate the Correlation Coefficient Given a Scatter Plot
This video explains how to estimate the correlation coefficient given a scatter plot.
From playlist Performing Linear Regression and Correlation
Line of Best Fit (Determining the equation)
More resources available at www.misterwootube.com
From playlist Descriptive Statistics & Bivariate Data Analysis
Scatterplots, Part 3: The Formula Behind the Correlation Coefficient
We use the Scatterplots & Correlation app to explain the formula behind the correlation coefficient. The app allows you to find and plot the z-scores, showing the 4 quadrants in which points on the scatterplot can fall.
From playlist Chapter 3: Relationships between two variables
Factoring a polynomial when a is greater than one using box method
👉Learn how to factor quadratics when the coefficient of the term with a squared variable is not 1. To factor an algebraic expression means to break it up into expressions that can be multiplied together to get the original expression. To factor a quadratic trinomial where the coefficient
From playlist Factor Quadratic Trinomials a=2 | 5 Examples
Asymptotic properties of the volatility estimator from high-frequency data modeled by Ananya Lahiri
Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst
From playlist Large deviation theory in statistical physics: Recent advances and future challenges
R - Exploratory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Fall 2020 https://www.patreon.com/statisticsofdoom This video is part of my structural equation modeling class - you will learn how to perform an exploratory factor analysis as a way to ease into the ideas of SEM. You will learn how to assess the number of
From playlist Structural Equation Modeling 2020
Stanford CS229: Machine Learning | Summer 2019 | Lecture 18 - Principal & Independent CA
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3E9HJHU Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Optimal Mixing of Glauber Dynamics: Entropy Factorization via High-Dimensional Expan - Zongchen Chen
Computer Science/Discrete Mathematics Seminar I Topic: Optimal Mixing of Glauber Dynamics: Entropy Factorization via High-Dimensional Expansion Speaker: Zongchen Chen Affiliation: Georgia Institute of Technology Date: February 22, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2017 This video covers all the tests available in the course so you can have a better idea of which ones to pick. Lecture materials and assignment available at statstools.com.
From playlist Advanced Statistics Videos
Neuroscience source separation 2a: Spatial separation
This is part two of a three-part lecture series I taught in a masters-level neuroscience course in fall of 2020 at the Donders Institute (the Netherlands). The lectures were all online in order to minimize the spread of the coronavirus. That's good for you, because now you can watch the en
From playlist Neuroscience source separation (3-part lecture series)
Statistical modelling of Dengue incidences and climatic variables in India by Ravishankar N
DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore
From playlist Mathematical and statistical explorations in disease modelling and public health
New physics searches in Neutrino Oscillations by Poonam Mehta
DISCUSSION MEETING : PARTICLE PHYSICS: PHENOMENA, PUZZLES, PROMISES ORGANIZERS: Amol Dighe, Rick S Gupta, Sreerup Raychaudhuri and Tuhin S Roy, Department of Theoretical Physics, TIFR, India DATE: 21 November 2022 to 23 November 2022 VENUE: Ramanujan Lecture Hall and Online While the L
From playlist Particle Physics: Phenomena, Puzzles, Promises - (Edited)
David Heckerman, Microsoft - Stanford Big Data 2015
Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.
From playlist Big Data in Biomedicine Conference 2015
Nonnegative matrix factorisation with the beta-divergence (...) - Févotte - Workshop 3 -CEB T1 2019
Cédric Févotte (CNRS, Toulouse) / 04.04.2019 Nonnegative matrix factorisation with the beta-divergence for robust hyperspectral unmixing Data is often available in matrix form, in which columns are samples, and processing of such data often entails finding an approximate factorisation o
From playlist 2019 - T1 - The Mathematics of Imaging
EFFECT Size for Correlation: Coefficient of Determination (7-3)
The Correlation Coefficient is also an Effect Size. An r value can be squared to calculate an effect size. The r-squared is the Coefficient of Determination, expressing the proportion of variance in the dependent variable (Y) explained by variance in the independent variable (X). The rever
From playlist Correlation And Regression in Statistics (WK 07 - QBA 237)