1. Statistical Data Analysis STAT221A
Statistical Data Analysis - Lecture /03/03
Statistical Data Analysis STAT221A
Dr. Judi McWhirter
Room G3.29 – third floor of G block
Office hours: by appointment
Web page for this course
We will use this website for distribution of lecture notes, assignments, and related documents or computer files
Statistical Data Analysis - Lecture /03/03

2. Statistical Data Analysis - Lecture 1 - 04/03/03
Course Structure
Data Exploration, Presentation, and Analysis
(3 weeks)
Analysis of Variance (ANOVA) and Design (3 weeks)
Regression (3 weeks)
Multivariate data (3 weeks)
Statistical Data Analysis - Lecture /03/03

3. Statistical Data Analysis - Lecture 1 - 04/03/03
Textbooks
There are no set texts for this course, but you might like to read:
A.J. Lee - Data analysis – an introduction using R
This book will be on desk copy in the library and is currently out of print
Don’t use this as a reference for R – the commands used are extras written especially for that book
Peter Dalgaard – Introductory Statistics using R
This book will be on desk copy in the library.
Moore & McCabe – Introduction to the practice of Statistics 3rd Ed.
This book is on desk copy in the library. If you took last year you should already own a copy.
Statistical Data Analysis - Lecture /03/03

4. Computer Laboratories
Statistical Data Analysis - Lecture /03/03
Computer Laboratories
Lab is Lab 5 in R Block, room RG.12
Login names/User names will be on the R block notice board
If your name is not on the board see Harry Johnston, room RG.20
Get in early! Inability to get to the computer lab is not a valid excuse for late assignments
Statistical Data Analysis - Lecture /03/03

5. Statistical Data Analysis - Lecture 1 - 04/03/03
Lab times
There is a space reserved (sometimes with demonstrators) for our class on:
Tuesday 11am-1pm
Thursday 2pm-4pm
Friday 2pm-4pm
Statistical Data Analysis - Lecture /03/03

6. Statistical Data Analysis - Lecture 1 - 04/03/03
Assessment
Internal assessment
Computing assignments 80%
One test during class time (30th April) 20%
Exam/internal assessment
Ratio 1:1
Late assignments get zero. Medical certificates/Counsellors certificates are the only excuse
You must get over 40% for your coursework to get credit for it.
Statistical Data Analysis - Lecture /03/03

7. Statistical Data Analysis - Lecture 1 - 04/03/03
Some aims of course
Learn more about how statistics can help solve real problems.
Including how to figure out what the problem really is!
What the statistical result means!
Learn more about communicating the results of statistical manipulations to the ‘client’,
Gain and improve specific skills in statistical analysis eg. regression, analysis of variance, multivariate analysis,
Statistical Data Analysis - Lecture /03/03

8. Statistical Data Analysis - Lecture 1 - 04/03/03
Some aims of course
But NOT to learn mathematical theorems which may be behind statistical procedures
Note the emphasis is on practical statistical inference.
R will be used to perform all statistical calculations. We will also use Excel for data management.
R –
Statistical Data Analysis - Lecture /03/03

9. The statistical process
Statistical Data Analysis - Lecture /03/03
The statistical process
Population
Sampling
Sample
The statistician
Calculating
Inferring
Estimating
Sample Estimate
Population value
Statistical Data Analysis - Lecture /03/03

10. Statistical inference about a population
Statistical Data Analysis - Lecture /03/03
Statistical inference about a population
Population -The entire set of things we wish to make statements about.
Sample--The set of things we have data from
Statistical inference--making probabilistic statements about population parameters based on sample statistics.
This requires that the sample was chosen randomly by some probabilistic method
Statistical Data Analysis - Lecture /03/03

11. Exploration and presentation of univariate data
Statistical Data Analysis - Lecture /03/03
Exploration and presentation of univariate data
Single variable. (there may be multiple samples of the same variable)
E.g. Reported rapes for 2001 in NZ
We could group these into regions.
E.g. Results of “Sex, Drugs, & Rock n Roll” class survey.
We will look at each of the questions separately.
We will look at responses grouped into males and females.
Statistical Data Analysis - Lecture /03/03

12. Statistical inference about a relationship
Statistical Data Analysis - Lecture /03/03
Statistical inference about a relationship
We have observations on two or more variables for a number of items.
We believe there is an underlying (linear) relationship between the variables, but we don’t know the parameters.
Assuming a random error structure on the observations.
We make inferences about the parameters of the relationship.
Whether or not the conclusions extend to the population of items depends how the ones we measured were selected.
Statistical Data Analysis - Lecture /03/03

13. Inference on a relationship
Statistical Data Analysis - Lecture /03/03
Inference on a relationship
Observational study--which items go into the treatment group(s) and the control group are not determined using randomisation.
Randomised Experiment--we use randomisation to determine which items go into treatment groups and which go into control group.
We can infer the relationship is a causal one only when the data comes from a randomised experiment.
Statistical Data Analysis - Lecture /03/03

14. Sex Drugs & Rock n Roll Data
Statistical Data Analysis - Lecture /03/03
Sex Drugs & Rock n Roll Data
Census (population : students taking first year statistics 121 or 122 at Waikato) data with errors (incorrect responses)
Class was surveyed using randomised response technique.
Each person responded either to sensitive or dummy question, depending on outcome of roll of dice.
Data contains incorrect responses.
Did not get individual information from the data
The incorrect responses are based on probability, so we can make inferences about population parameters from the data.
Statistical Data Analysis - Lecture /03/03

15. Questions to think about
Statistical Data Analysis - Lecture /03/03
Questions to think about
Can we generalise the conclusions from population of students taking first year statistics course 121 or 122 at Waikato to any wider population?
What population would you think it could be generalised to?
On what basis could you justify generalising it?
Statistical Data Analysis - Lecture /03/03