Matlab mini-course information

Place and Time

Material Covered

Lecture Slides Chapter Extras
01 Introduction 1  
02 Simple linear regression model; least squares; residuals 2  
03 Introduction to MATLAB   demo.m
plot_gpa_fit.m
my_regress.m
CH01PR19.txt
Getting started guide
04 Normal error regression model; maximum likelihood    
05 Confidence intervals and hypothesis testing in the normal regression model.    
06 Proof of Gauss Markov Theorem    
07 Inference in Normal Regression Model 2.7-2.10  
08 ANOVA   Cochran’s theorem
09 Diagnostics and Remedial Measures 3  
10 Remedial Measures and Transformations 4  
11 Joint estimation, Bonferroni joint confidence intervals 5  
12 Linear Algebra Review   Cheat sheets
Matrix, Gaussian, Linear Algebra
13 cont.    
14 cont.    
15 Multivariate Normal Review    
16 cont.    
17 Matrix Linear Regression 5  
18 Multiple linear regression, Testing 6  
19 Proof of Cochran’s Theorem, extra    
20 ANOVA 7  
21 Quantitative and Qualitative Inputs, Interactions, and Interpretation 8  
22 Midterm    
23 Principal Components Analysis    
24 In class group, project proposal highlights. One page project summary due.    
25 (*) Generalized Linear Models    
26 (*)    
27 TBD    
Term: Fall 2010
Time: Tu-Th, 10:30am-12pm
Location : Mathematics 417
Professor: Frank Wood
Email: fwood@stat.columbia.edu
Office:
Room 1017
School of Social Work
Hours:
11am-1pm Wed
TA: Wei Wang
Email: ww2243@columbia.edu
Office:
901
School of Social Work
Hours:
9-10am Wed
2-3pm Wed