Least squares | Regression models
In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples. For simple linear regression the effect is an underestimate of the coefficient, known as the attenuation bias. In non-linear models the direction of the bias is likely to be more complicated. (Wikipedia).
C33 Example problem using variation of parameters
Another example problem using the method of variation of parameters on second-order, linear, ordinary DE's.
From playlist Differential Equations
Linear Regression using Python
This seminar series looks at four important linear models (linear regression, analysis of variance, analysis of covariance, and logistic regression). A video that explains all four model types is at https://www.youtube.com/watch?v=SV9AxXFWZnM&t=12s This video is on linear regression usin
From playlist Statistics
Brief intro the the linear regression formula and errors.
From playlist Regression Analysis
C32 Example problem using variation of parameters
Another example problem using the method of variation of parameters.
From playlist Differential Equations
Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/
From playlist Experimental Design
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
B05 Local truncation errors in numerical analysis
From playlist A Second Course in Differential Equations
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.
B15 Example problem with a linear equation using the error function
Solving an example problem for a linear equation with the error function.
From playlist Differential Equations
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 about SEM terminology, degrees of freedom, specification, and start to see some lavaan output. You can learn more at: https://
From playlist Structural Equation Modeling 2020
2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Ashenfelter's linear regression model. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT 15.071 The Analytics Edge, Spring 2017
Statistical Rethinking 2022 Lecture 17 - Measurement Error
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: Music: https://www.youtube.com/watch?v=xXHH6bBAjDQ Palms: https://www.youtube.com/watch?v=We2KHqtqDos Pancake: https://www.youtube.com/watch?v=44ORuxym4fo Pause: https://www.youtube.com/watch?v=p
From playlist Statistical Rethinking 2022
Data Science - Part XII - Ridge Regression, LASSO, and Elastic Nets
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of some modern regression techniques including a discussion of the bias varianc
From playlist Data Science
Introduction to R: Linear Regression
This lesson covers the basics of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models. This is lesson 27 of a 30-
From playlist Introduction to R
R - Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the basics of confirmatory factor analysis or measurement models. You will learn about how to build, analyze, summarize, and diagram a measurement model in lavan. You can learn more at:
From playlist Structural Equation Modeling 2020
Mod-13 Lec-35 Measurement Errors and Calibration Problem
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
R - Hierarchical Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Spring 2021 https://www.patreon.com/statisticsofdoom This video covers the second round of confirmatory factor analysis or measurement models. You will learn how to create a hierarchical model and a bifactor CFA model, along with the special considerations
From playlist Structural Equation Modeling 2020
Regression assumptions explained!
See all my videos at http://www.zstatistics.com/ See the whole regression series here: https://www.youtube.com/playlist?list=PLTNMv857s9WUI1Nz4SssXDKAELESXz-bi 0:00 Introduction 8:08 Linearity (correct functional form) 14:10 Constant error variance (homoskedasticity) 19:18 Independent e
From playlist Regression series (10 videos)
From playlist STAT 501