Curve fitting | Estimation theory | Parametric statistics | Single-equation methods (econometrics)
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis. Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine. Linear regression has many practical uses. Most applications fall into one of the following two broad categories: * If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response. * If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response. Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the "lack of fit" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms "least squares" and "linear model" are closely linked, they are not synonymous. (Wikipedia).
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.
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
(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
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.
(ML 9.1) Linear regression - Nonlinearity via basis functions
Introduction to linear regression. Basis functions can be used to capture nonlinearities in the input variable. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
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
Simple Linear Regression Formula, Visualized | Ch.1
In this video, I will guide you through a really beautiful way to visualize the formula for the slope, beta, in simple linear regression. In the next few chapters, I will explain the regression problem in the context of linear algebra, and visualize linear algebra concepts like least squa
From playlist From Linear Regression to Linear Algebra
Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=MachineLearning-NUXdtN1W1FE&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootc
From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]
Linear Regression Algorithm | Linear Regression in Python | Machine Learning Algorithm | Edureka
** Machine Learning Training with Python: https://www.edureka.co/data-science-python-certification-course ** This Linear Regression Algorithm video is designed in a way that you learn about the algorithm in depth. This video is designed in a way that in the first part you will learn about
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Python for Data Analysis: Linear Regression
This video covers the basics of linear regression and how to perform linear regression in Python. Subscribe: â–º https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 27 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. L
From playlist Python for Data Analysis
Introduction to R: Linear Regression
This lesson covers the basics of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models. This is lesson 27 of a 30-
From playlist Introduction to R
Logistic Regression in R | Logistic Regression in R Example | Data Science Algorithms | Simplilearn
🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=DescriptionFirstFold&utm_source=youtube This Logistic Regression in R video will help you understand what is a regression, the need for regression and th
From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]
Logistic Regression - Is it Linear Regression?
Is it Linear? Why the sigmoid? Let's talk about it. Breaking Linear Regression video: https://www.youtube.com/watch?v=Bu1WCOQpBnM RESOURCES [1] Great Lecture notes to start understanding Logistic Regression: https://pages.stat.wisc.edu/~st849-1/lectures/GLMH.pdf [2] More slightly detaile
From playlist Logistic Regression
Linear Regression in R | Linear Regression in R With Example | Data Science Algorithms | Simplilearn
This Linear regression in R video will help you understand what is linear regression, why linear regression, and linear regression in R with example. You will also look at a use case predicting the revenue of a company using multiple linear regression. Now, let's deep dive into this video
From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]
Data science in Python: pandas, seaborn, scikit-learn
In this video, we'll cover the data science pipeline from data ingestion (with pandas) to data visualization (with seaborn) to machine learning (with scikit-learn). We'll learn how to train and interpret a linear regression model, and then compare three possible evaluation metrics for regr
From playlist Machine learning in Python with scikit-learn
Regression Analysis | What Is Regression Analysis | Introduction to Regression Analysis |Simplilearn
In this video we are going to cover how regression analysis perform by SVM algorithm using Olympic 2022 dataset. This video will help you to understand what is machine learning, what is supervised learning, types of supervised learning - Classification and Regression, What is regression a
Machine Learning Algorithms In-Depth Guide For 2022 | ML Algorithms Explained | Simplilearn
This video on Machine Learning Algorithm will take you through a detailed concept of machine learning algorithm. This video will help you to understand What is an Algorithm, What is Machine Learning, Types of Machine Learning, How Algorithms works in Machine Learning/Programing, Some popul
Digging into Data: Linear and Regularized Regression
Making predictions about real-valued data.
From playlist Digging into Data