Hat Matrix In Regression

Where Y is the outcome with X as predictors.

Hat matrix in regression. We call this the hat matrix because is turns Ys into Ys. Matrix notation applies to other regression topics including fitted values residuals sums of squares and inferences about regression parameters. A projection matrix known as the hat matrix contains this information and together with the Studentized residuals provides a means of.

Y-axis represents standardized residuals. Simple Linear Regression in Matrix Format 36-401 Section B Fall 2015 13 October 2015 Contents. What is the Hat matrix known in econometrics as the projection matrix P in regressionA picture of what the Hat matrix does in regressionHow does the hat m.

Visually by contrasting two di erent viewpoints on multiple linear regression. It is useful for investigating whether one or more observations are outlying with regard to their X values and therefore might be excessively influencing the regression results. Properties and Interpretation Week 5 Lecture 1 1 Hat Matrix 11 From Observed to Fitted Values.

The hat matrix in regression is just another name for the projection matrix. It is defined as the matrix that converts values from the observed variable into estimations obtained with the least squares method. Estimated Covariance Matrix of b This matrix b is a linear combination of the elements of Y.

Lets look at some of the properties of the hat matrix. I understand that the trace of the projection matrix also known as the hat matrix XInv XXX in linear regression is equal to the rank of X. The hat matrix is a matrix used in regression analysis and analysis of variance.

These estimates will be approximately normal in general. Working paper Sloan School of Management. These estimates are normal if Y is normal.