YouTube Linear Regression Courses List

This course on Linear Regression Analysis and Forecasting provides an in-depth look at the tools and techniques used to find the statistical model between input and output variables. Learn how to use regression analysis to forecast future outcomes, and understand the steps and checks required to obtain a good model. With this course, you will gain the skills to accurately predict future outcomes and make informed decisions. View all

This course is perfect for anyone looking to learn about linear regression and linear models. StatQuest provides an in-depth look at fitting a line to data, least squares, linear regression, multiple regression, R-squared, P Values, T-tests, and ANOVA. With clear explanations and examples, this course is the perfect way to gain a better understanding of linear regression and linear models. Click now to get started! View all

Simple Linear Regression is a statistical technique used to predict the value of a dependent variable based on the value of an independent variable. It is based on the Least Squares Regression Line, which is used to interpret the model parameters. Assumptions are made to ensure the accuracy of the model, and these assumptions are checked to ensure the validity of the results. This course provides an introduction to Simple Linear Regression and its various components. View all

This course introduces students to linear models, beginning with simple linear regression. It covers the properties of least squares estimators, estimating the residual variance, and matrix notation. It also covers multiple linear regression, diagnostics, and model selection. Finally, it covers generalized linear models and their applications. View all

This tutorial by Great Learning provides a comprehensive overview of the Linear Regression Algorithm, Linear Regression Machine Learning, and Linear Regression Full Course. Professor Mukesh Rao, the academic director at Great Learning, will guide learners through the concepts and help them gain a better understanding of the topic. This tutorial is ideal for those looking to gain a deeper understanding of Linear Regression. View all

Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It involves calculating the sum of squared errors (SSE), sum of squared regression (SSR) and sum of squared total (SST) to determine the R-squared value. Degrees of freedom, adjusted R-squared and non-linear relationships are also discussed in the series. Additionally, logarithms and advanced regression techniques are explored. View all

In this series of tutorials, MarinStatsLectures covers the basics of linear regression in R. Tutorial 5.1 covers simple linear regression, 5.2 covers checking linear regression assumptions, and 5.3 covers multiple linear regression. The tutorials provide an overview of the linear regression process, from data preparation to model fitting and interpretation. View all

This course covers the background material for General Linear Models. Topics include random vectors and random matrices, statistical distributions such as central and noncentral t and chi square df=1 distributions, deriving the F distribution, noncentral F distribution, idempotent matrices, independence of quadratic forms, distribution of quadratic forms, (1-a)% confidence region for a multivariate mean vector, derivative of a quadratic form with respect to a vector, projection matrices, mean, variance, and covariance of quadratic forms, a square-root matrix, inverse of a partitioned matrix, the spectral decomposition, Woodbury matrix identity and Sherman-Morrison formula, generalized inverse matrix, generalized inverse for a symmetric matrix, Gram-Schmidt orthonormalization process, and sum of perpendicular projection matrices. View all

Generalized Linear Models (GLMs) are a type of statistical model used to analyze data. They are based on a canonical link function and use likelihood, score, and Fisher Information to estimate parameters. An iteratively re-weighted least squares method is used for a general link function. GLMs are used to model a wide range of data types, including binary, count, and continuous data. View all

This Linear Regression Tutorial will introduce you to the concept of linear regression and help you understand what regression analysis is and how to implement simple and multiple linear regression using Python. It will focus on two main topics: Simple Linear Regression and Multiple Linear regression. With step-by-step examples, you will learn to compute all of the essential outputs for simple linear regression and multiple regression and be able to correctly interpret the outputs you produce. Learn to ace your skills in Linear Regression and understand the whole process with examples and visuals. Click now to get started! View all

This course introduces the design of experiments and the use of general linear models. It covers topics such as projection matrices onto c(X), least square estimator of Beta, and estimability. It provides examples and explanations to help students understand the concepts and apply them to their own experiments. View all

This video provides an introduction to Linear Regression Analysis, a powerful Machine Learning algorithm used for predictive analysis. It covers the fundamentals of the algorithm and how to implement it in Python. It is a great resource for those looking to gain a better understanding of Linear Regression and its applications. View all

This tutorial by Great Learning provides a comprehensive overview of Linear Regression. It covers the underlying concepts and teaches users how to implement the model in both R and Python. It is an ideal resource for those looking to gain a better understanding of this machine learning technique. View all

This video series covers the concept of linear regression and its implementation in R. It begins with an overview of simple linear regression, followed by a tutorial on how to use R to perform linear regression. The series then explains how to check linear regression assumptions in R. Finally, it provides an example of linear regression in R. This series is a great resource for anyone looking to learn more about linear regression and its implementation in R. View all

This course covers the fundamentals of linear regression analysis and forecasting, including standardized regression coefficients, testing of hypothesis, simple linear regression analysis, and software implementation in a simple linear regression model using MINITAB. Additionally, it covers estimation of model parameters in multiple linear regression models. View all

Gain an introduction to Linear Regression in Machine Learning what is linear regression Machine Learning Tutorial View all