Parametric statistics | Least squares
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable. Geometrically, this is seen as the sum of the squared distances, parallel to the axis of the dependent variable, between each data point in the set and the corresponding point on the regression surface—the smaller the differences, the better the model fits the data. The resulting estimator can be expressed by a simple formula, especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent for the level-one fixed effects when the regressors are exogenous and forms perfect colinearity (rank condition), consistent for the variance estimate of the residuals when regressors have finite fourth moments and—by the Gauss–Markov theorem—optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated. Under these conditions, the method of OLS provides minimum-variance mean-unbiased estimation when the errors have finite variances. Under the additional assumption that the errors are normally distributed with zero mean, OLS is the maximum likelihood estimator that outperforms any non-linear unbiased estimator. (Wikipedia).
Least squares method for simple linear regression
In this video I show you how to derive the equations for the coefficients of the simple linear regression line. The least squares method for the simple linear regression line, requires the calculation of the intercept and the slope, commonly written as beta-sub-zero and beta-sub-one. Deriv
From playlist Machine learning
Determine a Least Squares Solutions to Ax=b
This video explains how to determine a least-squares solutions to Ax=b that has no solution.
From playlist Least Squares Solutions
Ordinary least squares tutorial using Julia
In this video tutorial I develop an inituitive understanding of ordinary least squares (OLS) and how it pertains to finding a quadratic equation, a cubic equation, and a linear equation given three point in the plane. Developing an intuition for OLS requires understanding of the column sp
From playlist Julia on Coursera
Least-squares fitting is one of the most important matrix algebra techniques in statistics and scientific computing. You'll learn the theory of least-squares fitting and see several examples in simulated and in real data. The video uses files you can download from https://github.com/mikex
From playlist OLD ANTS #9) Matrix analysis
Ordinary Least Squares Tutorial using Python
In this video tutorial I discuss the creation of a quadratic, a cubic, and a linear equation given three points in the plane. These approximations (solution in the case of the cubic equation) can be calculated using ordinary least squares (OLS). In the video I develop and intuitive and vi
From playlist Coursera - Understanding Clinical Research
The Least Squares Formula: A Derivation
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 4 Linear Algebra: Inner Products
Discrete Math - 4.3.2 Greatest Common Divisors and Least Common Multiples
Finding the greatest common divisor and least common multiple using the method of primes. Textbook: Rosen, Discrete Mathematics and Its Applications, 7e Playlist: https://www.youtube.com/playlist?list=PLl-gb0E4MII28GykmtuBXNUNoej-vY5Rz
From playlist Discrete Math I (Entire Course)
On the number of ordinary lines determined by sets in complex space - Shubhangi Saraf
Computer Science/Discrete Mathematics Seminar I Topic: On the number of ordinary lines determined by sets in complex space Speaker: Shubhangi Saraf Affiliation: Rutgers University Date: December 5, 2016 For more video, visit http://video.ias.edu
From playlist Mathematics
Transformation and Weighting to correct model inadequacies (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
Transformation and Weighting to correct model inadequacies (Part C)
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
Minerva Lectures 2013 - Terence Tao Talk 1: Sets with few ordinary lines
For more information please visit: http://math.princeton.edu/events/seminars/minerva-lectures/minerva-lecture-i-sets-few-ordinary-lines
From playlist Minerva Lecture Terence Tao
This video provides an explanation on how to determine the LCM or least common multiple of two integers. http://www.mathispower4u.com
From playlist Factors, Prime Factors, and Least Common Factors
Geostatistics session 3 universal kriging
Introduction to Universal Kriging
From playlist Geostatistics GS240
Linear regression: OLS coefficients minimize the SSR (FRM T2-15)
[my XLS is here https://trtl.bz/2uiivIm] The ordinary least squares (OLS) regression coefficients are determined by the "best fit" line that minimizes the sum of squared residuals (SSR). Discuss this video in our FRM forum! https://trtl.bz/2Kn3uJJ Subscribe here https://www.youtube.com/c
From playlist Quantitative Analysis (FRM Topic 2)
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
Mod-12 Lec-34 Regression Models with Autocorrelated Errors (Contd.)
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
Ordinary Least Squares Regression
From playlist Statistical Regression
Ex 2: Determine the Least Common Multiple Using a Fraction Wall or Rods
This video explains how to determine the least common multiple of two whole numbers using a fraction wall or rods. Site: http://mathispower4u.com
From playlist Factors, LCM, and GCF of Whole Numbers
Regression with Machine Learning with Jon Krohn
Jon Krohn provides practical real-world demonstrations of regression, a powerful, highly extensible approach to making predictions. He distinguishes independent from dependent variables and discusses linear regression to predict continuous variables, first with a single model feature, and
From playlist Talks and Tutorials