Linear Regression: What is Linear Regression in Machine Learning and DS | Theory + Practical Using R скачать в хорошем качестве

Linear Regression: What is Linear Regression in Machine Learning and DS | Theory + Practical Using R

In this video theory and practical knowledge of linear regression using R is explained.In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. ---------------------------------------------------- Video Timeline 00:00 Introduction. 00:52 Linear Regression Theory. 02:11 Types of Linear Regression. 03:10 Scatter Plot For Linear Regression. 04:25 Problem Statement. 06:04 R Code For Linear Regression. 12:45 Results. ---------------------------------------------------- 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: 1) If the goal is prediction, forecasting, or error reduction,[clarification needed] 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. 2) 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. ----------------------------------------------------- Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variables and their relationship. Numerous extensions have been developed that allow each of these assumptions to be relaxed (i.e. reduced to a weaker form), and in some cases eliminated entirely. Generally these extensions make the estimation procedure more complex and time-consuming, and may also require more data in order to produce an equally precise model. ----------------------------------------------------- Regression analysis is a very widely used statistical tool to establish a relationship model between two variables. One of these variable is called predictor variable whose value is gathered through experiments. The other variable is called response variable whose value is derived from the predictor variable.In Linear Regression these two variables are related through an equation, where exponent (power) of both these variables is 1. Mathematically a linear relationship represents a straight line when plotted as a graph. A non-linear relationship where the exponent of any variable is not equal to 1 creates a curve. The general mathematical equation for a linear regression is = y = ax + b. ----------------------------------------------------- lm() FunctionThis function creates the relationship model between the predictor and the response variable. ----------------------------------------------------- Instructor : Priyanka Singh ----------------------------------------------------- Follow us on Twitter : https://twitter.com/Forerun27232724?s=09 Facebook :   / forerunners-110750477376350   Instagram : https://www.instagram.com/invites/con...