ST2137 Computer Aided Data Analysis

Module Description
This module introduces students to the statistical computer packages, with main focus on SAS, Splus and SPSS, that provide the computational tools for performing statistical data analysis using the methodology covered in the prerequisite modules. Topics include data access, transformations, estimation, testing hypotheses, ANOVA, performing resampling methods and simulations. It also equips students with basic computational techniques for maximum likelihood estimation. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

Contrary to what was written in the module description, I was taught R instead of Splus. I believe that is the right change because R is already more popular.

Topics
- Introduction to R
- Introduction to SAS
- Introduction to SPSS
- Describing Numerical Data
- Robust Statistics for Location and Scale Parameters
- Analyzing Categorical Data
- One Sample Tests and Two Sample Tests
- One-way Analysis of Variance
- Simulation: An Introduction
- Simulation Studies in Statistics
- Resampling: Bootstrap Method
- Numerical Methods in R
- Regression Analysis

This is an extremely informative module in my honest opinion. As a statistics major, it is always good to know a few statistical softwares especially R and SPSS.

Core for statistics major.

Workload
Two two-hours lecture
I was taught by Dr David Chew. In general, I think he is alright at teaching but he is pretty boring. He provides different versions of the lecture notes (slide or pdf) for you to print out.

Weekly one-hour tutorial
All tutorials try to familiarise you with the usage of R, SAS and SPSS. Each tutorial will require you to head down to the computer laboratories because SAS and SPSS are not free for students to download (R is free). This will be annoying for students who are not staying in campus. Tutorials are not difficult but may be time-consuming.

Textbooks are not needed
Lecture notes are sufficient. Textbooks are available for R, SPSS and SAS but they are all pretty expensive. No real need to buy.

Lectures were not webcasted

Assessment (AY12/13 Sem 1)
20% Project (Consist of 2 parts, may form groups of 7-8. It is advisable to split the part 1 and 2 to different people to save time)
10% Laboratory Test (Not very difficult as long as you study. All softwares might tested but only 1 will be tested (random))
20% Midterm (Open-ended)
50% Final (Open-ended)
Cheat sheets allowed

Extra notes for readers: I have the lecture notes, sample paper and my project. I won't give out my project but I don't mind helping anyone check their project. If anyone is interested, please leave a comment and check back often. I will get back to you as soon as I can :D
If you want to know more about the modules I have taken, you may refer to this list or the labels on the right! :D