Lecture 13: Simple Linear Regression in Matrix Format

Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression

In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

A matrix method for estimating linear regression coefficients based on fuzzy numbers

In this paper, a new method for estimating the linear regression coefficients approximation is presented based on Z-numbers. In this model, observations are real numbers, regression coefficients and dependent variables (y) have values ​​for Z-numbers. To estimate the coefficients of this model, we first convert the linear regression model based on Z-numbers into two fuzzy linear regression mode...

Lecture 5 Design of Experiments and Confidence

In this lecture I’d like to discuss some very general considerations having to do with design of experiments, relating them to the linear regression model and eventually to their implications for making inferences about models with elliptical confidence regions. In light of these considerations, it is perhaps useful to review some basic facts about confidence regions for parameters in the class...

Randomized Algorithms for Matrices and Data Lecture 1 - 09 / 04 / 2013 Lecture 1 : Introduction and Overview

This course will cover recent developments in randomized matrix algorithms of interest in large-scale machine learning and statistical data analysis applications. By this, we will mean basic algorithms for fundamental matrix problems—such as matrix multiplication, least-squares regression, lowrank matrix approximation, and so on—that use randomization in some nontrivial way. This area goes by t...

Lecture 7 : The Simple Linear Regression Model : Hypothesis Testing by Professor Scott H . Irwin Required