Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression.Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced /ˈloʊɛs/. They are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model.In some fields, LOESS is known and commonly referred to as Savitzky–Golay filter (proposed 15 years before LOESS). LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression. They address situations in which the classical procedures do not perform well or cannot be effectively applied without undue labor. LOESS combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. It does this by fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point. In fact, one of the chief attractions of this method is that the data analyst is not required to specify a global function of any form to fit a model to the data, only to fit segments of the data. The trade-off for these features is increased computation. Because it is so computationally intensive, LOESS would have been practically impossible to use in the era when least squares regression was being developed. Most other modern methods for process modeling are similar to LOESS in this respect. These methods have been consciously designed to use our current computational ability to the fullest possible advantage to achieve goals not easily achieved by traditional approaches. A smooth curve through a set of data points obtained with this statistical technique is called a loess curve, particularly when each smoothed value is given by a weighted quadratic least squares regression over the span of values of the y-axis scattergram criterion variable. When each smoothed value is given by a weighted linear least squares regression over the span, this is known as a lowess curve; however, some authorities treat lowess and loess as synonyms. (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.
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
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab
Regression Trees, Clearly Explained!!!
Regression Trees are one of the fundamental machine learning techniques that more complicated methods, like Gradient Boost, are based on. They are useful for times when there isn't an obviously linear relationship between what you want to predict, and the things you are using to make the p
From playlist StatQuest
Linear regression ANOVA ANCOVA Logistic Regression
In this video tutorial you will learn about the fundamentals of linear modeling: linear regression, analysis of variance, analysis of covariance, and logistic regression. I work through the results of these tests on the white board, so no code and no complicated equations. Linear regressi
From playlist Statistics
An introduction to Regression Analysis
Regression Analysis, R squared, statistics class, GCSE Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Linear Regression http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C Using SPSS for Multiple Linear Regression http://www.youtube.com/playlist?li
From playlist Linear Regression.
How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
From playlist Linear Regression.
CS231n Lecture 8 - Localization and Detection
ConvNets for spatial localization Object detection
From playlist CS231N - Convolutional Neural Networks
Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2ZdTL4x Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Lecture 3 | Machine Learning (Stanford)
Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BGwS/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic
From playlist Lecture Collection | Machine Learning
Linear Regression + Decision Trees = Linear Trees
Best of both worlds. My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
Simplified Machine Learning Workflows with Anton Antonov, Session #2: Quantile Regression (Part 2)
Anton Antonov presents the second session on quantile regression workflows in Wolfram Language.
From playlist Simplified Machine Learning Workflows with Anton Antonov
Statistical Learning: 7.4 Generalized Additive Models and Local Regression
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Lecture 01-02 Linear regression with one variable
Machine Learning by Andrew Ng [Coursera] 0105 Model representation 0106 Cost function 0107 Cost function intuition I 0108 Cost function intuition II 0109 Gradient descent 0110 Gradient descent intuition 0111 Gradient descent for linear regression 0112 What's next
From playlist Machine Learning by Professor Andrew Ng
Visualizing machine learning models in the Jupyter Notebook- Chakri Cherukuri (Bloomberg LP)
Chakri Cherukuri offers an overview of the interactive widget ecosystem available in the Jupyter notebook, including ipywidgets and bqplot, and illustrates how Jupyter widgets can be used to build rich visualizations of machine learning models. Along the way, Chakri walks you through algor
From playlist JupyterCon in New York 2018
How to do Simple Linear Regression by Hand (14-4)
Simple Linear Regression is used to predict the value of an output variable from a predictor variable. Although it is unlikely that you will be calculating many regression equations by hand, doing an example by hand is a great way to really understand regression. We will begin with the ass
From playlist WK14 Linear Regression - Online Statistics for the Flipped Classroom