Super Quick Review of Linear regression

Our data are a bunch of measurements of the variables xand y

A linear model of these data:

y = mx + b + noise

(we will solve for m and b)

If this model is true, then x and y are correlated.

Hernandez-Garcia, UM FMRI course

Super Quick Review of Linear regression

If m is “significant”, then we infer that the model is true.

Significant means that m is big enough compared to the noise.

Hernandez-Garcia, UM FMRI course

Super Quick Review of Linear regression

Say it with matrices

Y = X*b + e

best = (X)-1*Y

eest = Y – X*best

Tscore(1) = best(1) /eest(1)

Hernandez-Garcia, UM FMRI course

Super Quick Review of Linear regression

In Connectivity analysis:

The MODEL for all pixels is the time course of the “seed pixel”.

Hernandez-Garcia, UM FMRI course

Notes for today’s tutorial

When setting the path: do not use the GUI.

Instead put this in the command line

addpath C:\fmri_lab\FMRICourse\Lab3_Matlab\Mlib

addpath C:\SMP12

Hernandez-Garcia, UM FMRI course