The conditional change model in statistics is the analytic procedure in which change scores are regressed on baseline values, together with the explanatory variables of interest (often including indicators of treatment groups). The method has some substantial advantages over the usual two-sample t-test recommended in textbooks. (Wikipedia).
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
Model-Based Design for Predictive Maintenance, Part 6: Deployment of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 This video shows how prognostics models work, how they perform, and how you can deploy them. You’ll learn how to deploy a remaining useful life estimation model either as a standalone applicatio
From playlist Model-Based Design for Predictive Maintenance
Model-Based Design for Predictive Maintenance: Series Introduction
Learn the fundamental aspects of predictive maintenance. See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 MATLAB and Simulink for Predictive Maintenance: https://bit.ly/2Tp2yLq -----------------------------------------------------------------
From playlist Model-Based Design for Predictive Maintenance
How to Simulate Multiple Scenarios and Convert Models to Fixed Point | MATLAB & Simulink Developers
The Fixed-Point Tool in Simulink® can automatically explore compression choices to optimize your design based on high-level behavior constraints. The tool also uses multiple scenarios for simulation and verification. In this video, you will learn how the Fixed Point Tool can: combine full
From playlist Tips and Tricks from MATLAB and Simulink Developers
Model-Based Design for Predictive Maintenance, Part 5: Development of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 After performing real-time tests and validating your algorithm, you can use it to detect whether there are any mechanical or electrical issues in your system. However, you can also use condition
From playlist Model-Based Design for Predictive Maintenance
Model-Based Design for Predictive Maintenance, Part 1: Data Generation
Learn how physical modeling can help you generate synthetic failure data necessary for the development of your predictive maintenance algorithm. See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 You’ll first see how to use a Simulink® model a
From playlist Model-Based Design for Predictive Maintenance
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Lec 12 | MIT 2.830J Control of Manufacturing Processes, S08
Lecture 12: Full factorial models Instructor: Duane Boning, David Hardt View the complete course at: http://ocw.mit.edu/2-830JS08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 2.830J, Control of Manufacturing Processes S08
Some exact formulas in the integrable particle models with multi-species by Eunghyun Lee
PROGRAM :UNIVERSALITY IN RANDOM STRUCTURES: INTERFACES, MATRICES, SANDPILES ORGANIZERS :Arvind Ayyer, Riddhipratim Basu and Manjunath Krishnapur DATE & TIME :14 January 2019 to 08 February 2019 VENUE :Madhava Lecture Hall, ICTS, Bangalore The primary focus of this program will be on the
From playlist Universality in random structures: Interfaces, Matrices, Sandpiles - 2019
PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod
From playlist Data Assimilation Research Program
Universal Biology in Adaptation and Evolution: Multilevel Consistency, by Kunihiko Kaneko
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (ONLINE) ORGANIZERS: Debashish Chowdhury (IIT Kanpur), Ambarish Kunwar (IIT Bombay) and Prabal K Maiti (IISc, Bengaluru) DATE: 07 December 2020 to 18 December 2020 VENUE:Online 'Fluctuation-and-noise' are themes
From playlist Statistical Biological Physics: From Single Molecule to Cell (Online)
Adaptive Model Predictive Control Design with Simulink | Understanding MPC, Part 7
In this video, you will learn how to design an adaptive Model Predictive Control controller for an autonomous steering vehicle system whose dynamics change with respect to the longitudinal velocity. - Free Technical paper on Adaptive Cruise Controller with Model Predictive Control: http:/
From playlist Understanding Model Predictive Control
R - Latent Growth Models Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This video is a lecture that covers latent growth (curve) modeling - including the steps for random intercepts and slopes taken from the Beaujean SEM lavaan book. Lecture materials and assignment available at statistic
From playlist Structural Equation Modeling
Multilevel Latent Class Regression of Stages of Change for Multiple Health Behaviors
Multilevel Laten Class Regression of Stages of Change for Multiple Health Behaviors, recorded November 26th, 2012. For more information and access to courses, lectures, and teaching material, please visit the official UC Irvine OpenCourseWare website at: http://ocw.uci.edu
From playlist Public Health: Collections
James Thorson - Forecasting non-local climate impacts for mobile marine species using extensions...
Dr James Thorson (National Oceanic and Atmospheric Administration) presents "Forecasting non-local climate impacts for mobile marine species using extensions to empirical orthogonal function analysis", 8 May 2020.
From playlist Statistics Across Campuses
Physical Modeling Tutorial, Part 2: Simscape Fundamentals
Learn fundamental concepts of Simulink® like using foundation libraries, creating multidomain physical components, dividing components into subsystems, and setting initial conditions for physical variables. - Enter the MATLAB and Simulink Racing Lounge: http://bit.ly/2HhcXnU - Download E
From playlist Physical Modeling Tutorials
Feedback Control of Hybrid Dynamical Systems
Hybrid systems have become prevalent when describing complex systems that mix continuous and impulsive dynamics. Continuous dynamics usually govern the evolution of the physical variables in a system, while impulsive (or discrete) behavior is typically due to discrete events and abrupt cha
From playlist Complete lectures and talks: slides and audio
C28 Variation of parameters Part 1
We have already seen variation of parameters in action, but here we expand the method for use in second-order linear DE's, even with non-constant coefficients.
From playlist Differential Equations
Birth Rates and Death Rates in Differential Equations (Differential Equations 33)
https://www.patreon.com/ProfessorLeonard How changing Birth Rates and Death Rates can effect population growth and be modeled with Differential Equations.
From playlist Differential Equations