Regression analysis

Functional regression

Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified into four types depending on whether the responses or covariates are functional or scalar: (i) scalar responses with functional covariates, (ii) functional responses with scalar covariates, (iii) functional responses with functional covariates, and (iv) scalar or functional responses with functional and scalar covariates. In addition, functional regression models can be linear, partially linear, or nonlinear. In particular, functional polynomial models, functional single and multiple index models and functional additive models are three special cases of functional nonlinear models. (Wikipedia).

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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

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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.

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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.

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(ML 9.2) Linear regression - Definition & Motivation

Linear regression arises naturally from a sequence of simple choices: discriminative model, Gaussian distributions, and linear functions. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

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Linear Regression Using R

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.

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Linear regression (1): Basics

Basic form of a linear regression model; mean squared error loss; learning as optimization

From playlist cs273a

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What is Regression Testing? | Regression Testing in Software Testing | Edureka

** Test Automation Masters Program: https://www.edureka.co/masters-program/automation-testing-engineer-training ** This Edureka video on "What is Regression Testing?" will help you get in-depth knowledge on regression testing in software Testing and why it is important to incorporate re

From playlist Software Testing Training Videos | Edureka

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Linear regression: Sample Regression Function (SRF, FRM T2-14)

[my xls is here http://trtl.bz/2G8CSN3] In theory, there is one population (and one population regression function). Each sample varies and generates its own sample regression function (SRF). Therefore, the regression coefficients generated by the SRF are random variables; e.g., their stan

From playlist Quantitative Analysis (FRM Topic 2)

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Simplified Machine Learning Workflows with Anton Antonov, Session #5: Quantile Regression (Part 5)

Anton Antonov, a senior mathematical programmer, discusses the Quantile Regression Workflow in the Wolfram Language. You can find this content on the Wolfram Function Repository: https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression You can directly interact

From playlist Simplified Machine Learning Workflows with Anton Antonov

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Lecture 03-01 Logistic Regression

Machine Learning by Andrew Ng [Coursera] 0301 Classification 0302 Hypothesis Representation 0303 Decision boundary 0304 Cost function 0305 Simplified cost function and gradient descent 0306 Advanced optimization 0307 Multi-class classification: One-vs-all

From playlist Machine Learning by Professor Andrew Ng

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Tilmann Gneiting: Isotonic Distributional Regression (IDR) - Leveraging Monotonicity, Uniquely So!

CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 02, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians

From playlist Virtual Conference

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Predictive Modelling Techniques | Data Science With R Tutorial

🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=PredictiveModeling-0gf5iLTbiQM&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bo

From playlist R Programming For Beginners [2022 Updated]

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Data Science - Part XV - MARS, Logistic Regression, & Survival Analysis

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 on extending the regression concepts brought forth in previous lectures. We wi

From playlist Data Science

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Lecture 03-02 Regularization

Machine Learning by Andrew Ng [Coursera] 0308 The problem of overfitting 0309 Cost function 0310 Regularized linear regression 0311 Regularized logistic regression

From playlist Machine Learning by Professor Andrew Ng

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Applied Machine Learning: Secret Sauce

Professor Jann Spiess shares the secret sauce of applied machine learning.

From playlist Machine Learning & Causal Inference: A Short Course

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Ex: Comparing Linear and Exponential Regression

This video provides an example on how to perform linear regression and exponential regression on the TI84. The best model is identified based up the value of R^2. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com

From playlist Solving Applications Using Exponential Equations / Compounded and Continuous Interest / Exponential Regression

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(ML 19.9) GP regression - introduction

Introduction to the application of Gaussian processes to regression. Bayesian linear regression as a special case of GP regression.

From playlist Machine Learning

Related pages

Variance function | Polynomial regression | Regression analysis | Functional data analysis | Generalized functional linear model | Curse of dimensionality | Domain of a function | Semiparametric regression | Generalized linear model | Additive model | Variance | Linear regression | Orthonormal basis | Euclidean space | Nonlinear regression | Functional principal component analysis | Dependent and independent variables | B-spline | Hilbert space | Expected value | Lp space | Reproducing kernel Hilbert space | Basis function | Inner product space | Conditional expectation