Regression analysis | Errors and residuals | Statistical deviation and dispersion

Errors and residuals

In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "true value" (not necessarily observable). The error of an observation is the deviation of the observed value from the true value of a quantity of interest (for example, a population mean). The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals.In econometrics, "errors" are also called disturbances. (Wikipedia).

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What are Residuals in Regression?

Brief intro to residuals in regression. What they are and what they look like in relation to a line of best fit. Sum and mean of residuals.

From playlist Regression Analysis

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Outtakes

Yes. I make mistakes ... rarely. http://www.flippingphysics.com

From playlist Miscellaneous

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Residual plots and problems

I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: Understanding and interpreting residual plot

From playlist Older Statistics Videos and Other Math Videos

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What Are Error Intervals? GCSE Maths Revision

What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d

From playlist Error Intervals & Bounds GCSE Maths Revision

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Residual Analysis

Using residuals to analyze the data

From playlist Unit 3: Linear and Non-Linear Regression

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Systematic and Random Error

Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/

From playlist Experimental Design

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Intro to Linear Regression

Brief intro the the linear regression formula and errors.

From playlist Regression Analysis

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Signal reconstruction

A discrete signal has to be reconstructed to get back into the continuous domain.

From playlist Discrete

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Ensembles (3): Gradient Boosting

Gradient boosting ensemble technique for regression

From playlist cs273a

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Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 8 - non quadratic losses

Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/

From playlist Stanford EE104: Introduction to Machine Learning Full Course

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Genevieve Dusson - Error bounds for properties in planewave electronic structure calculations

Recorded 06 May 2022. Genevieve Dusson of the Université de Franche-Comté (Besançon), Laboratoire de Mathématiques, presents "Error bounds for properties in planewave electronic structure calculations" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstrac

From playlist 2022 Large-Scale Certified Numerical Methods in Quantum Mechanics

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Basic Excel Business Analytics #47: SST = SSR + SSE & R Squared & Standard Error of Estimate

Download files: https://people.highline.edu/mgirvin/AllClasses/348/348/AllFilesBI348Analytics.htm Learn: 1) (00:14) What we will do in this video: SST, SSR, SSE, R^2 and Standard Error 2) (00:44) What we did last video 3) (01:11) How do we think about “How good our Estimated Regression Li

From playlist Excel Business Analytics (Forecasting, Linear Programming, Simulation & more) Free Course at YouTube (75 Videos)

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Mod-12 Lec-33 Regression Models with Autocorrelated Errors

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

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Linear Regression Made Easy! The Epic Full Story with all Details. Excel Statistical Analysis 50

Download Excel File: https://excelisfun.net/files/Ch14-ESA.xlsm Download 2 PDF note files: https://excelisfun.net/files/Ch14-ESA.pptx, Download Deductive Proof 1 PDF: https://excelisfun.net/files/Linear%20Regression%20Slope%20Deductive%20Proof.pdf Download Deductive Proof 2 PDF (short ver

From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun

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DDPS | Towards reliable, efficient, and automated model reduction of parametrized nonlinear PDEs

Description: Many engineering tasks, such as parametric study and uncertainty quantification, require rapid and reliable solution of partial differential equations (PDEs) for many different configurations. In this talk, we consider goal-oriented model reduction of parametrized nonlinear PD

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

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Model Adequacy Checking (Part B)

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

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15b Machine Learning: Gradient Boosting

Lecture on ensemble machine learning with boosting with a demonstration based on tree based boosting.

From playlist Machine Learning

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Model Adequacy Checking (Part A)

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

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Bessel's correction | Univariate distribution | Basu's theorem | Almost surely | Regression analysis | Reduced chi-squared statistic | Mathematical optimization | Mean | Statistics | Chi-squared distribution | Sample mean | T-statistic | Regression dilution | Statistical population | Least absolute deviations | Probable error | Standard error | Mean absolute error | Confidence interval | Margin of error | Explained sum of squares | Innovation (signal processing) | Least squares | Elementary event | Student's t-distribution | Lack-of-fit sum of squares | Deviation (statistics) | Estimation | Linear regression | Observational error | Sampling error | Normal distribution | Standard deviation | Arithmetic mean | Type I and type II errors | Degrees of freedom (statistics) | Expected value | Studentized residual | Mean squared error | Econometrics | Sample variance | Error detection and correction