Design of experiments | Regression analysis
In statistics, multicollinearity (also collinearity) is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. In this situation, the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data set; it only affects calculations regarding individual predictors. That is, a multivariate regression model with collinear predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with respect to others. Note that in statements of the assumptions underlying regression analyses such as ordinary least squares, the phrase "no multicollinearity" usually refers to the absence of perfect multicollinearity, which is an exact (non-stochastic) linear relation among the predictors. In such a case, the design matrix has less than full rank, and therefore the moment matrix cannot be inverted. Under these circumstances, for a general linear model , the ordinary least squares estimator does not exist. In any case, multicollinearity is a characteristic of the design matrix, not the underlying statistical model. Multicollinearity leads to non-identifiable parameters. (Wikipedia).
Worldwide Calculus: Multi-Component Functions of a Single Variable
Lecture on 'Multi-Component Functions of a Single Variable' from 'Worldwide Multivariable Calculus'. For more lecture videos and $10 digital textbooks, visit www.centerofmath.org.
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Multivariable Calculus: Cross Product
In this video we explore how to compute the cross product of two vectors using determinants.
From playlist Multivariable Calculus
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We give the definition of differentiability for a multivariable function and provide a few examples. http://www.michael-penn.net https://www.researchgate.net/profile/Michael_Penn5 http://www.randolphcollege.edu/mathematics/
From playlist Multivariable Calculus
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From playlist Advanced Calculus / Multivariable Calculus
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A description of maxima and minima of multivariable functions, what they look like, and a little bit about how to find them.
From playlist Multivariable calculus
What is Multicollinearity? Extensive video + simulation!
See all my videos at http://www.zstatistics.com/videos/ 0:00 Introduction 2:16 Intuition 4:13 How does it affect our regression output? 6:55 Detection method I: Correlations 8:37 Detection method II: Variance Inflation Factors (VIFs) 11:50 Remedies 15:13 Justin's Simulation (COOL!) 22:17
From playlist Regression series (10 videos)
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
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
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)
2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Determining whether to keep all the variables in your final model. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses a
From playlist MIT 15.071 The Analytics Edge, Spring 2017
2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: Allison O'Hair Explores significant relationships between variables in the model. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses a
From playlist MIT 15.071 The Analytics Edge, Spring 2017
(PP 6.1) Multivariate Gaussian - definition
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From playlist Probability Theory
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From playlist How to Solve Multi Step Equations with Variables on Both Sides
Solving an equation with variables on both side and one solution
👉 Learn how to solve multi-step equations with variable on both sides of the equation. An equation is a statement stating that two values are equal. A multi-step equation is an equation which can be solved by applying multiple steps of operations to get to the solution. To solve a multi-s
From playlist Solve Multi-Step Equations......Help!
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From playlist Multivariable calculus
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3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models
MIT 15.071 The Analytics Edge, Spring 2017 View the complete course: https://ocw.mit.edu/15-071S17 Instructor: John Silberholz Building a regression model considering multicollinearity within independent variables. License: Creative Commons BY-NC-SA More information at https://ocw.mit.ed
From playlist MIT 15.071 The Analytics Edge, Spring 2017
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From playlist Advanced Calculus / Multivariable Calculus
R - Moderation Analyses (Manual) Example
Lecturer: Dr. Erin M. Buchanan Missouri State University Fall 2016 This video covers how to perform a moderation analysis manually, to be able to apply for 3+ way interactions. Other videos on our channel cover how to do moderation with the QuantPsyc package. Data screening (outliers, ho
From playlist Advanced Statistics Videos