1. Introduction to Statistical Analysis
Yale Braunstein
School of Information Management & Systems

2. Additional items related to MGO
Mission statements in the corporate world
A comparison of Coke & Pepsi
Refining objectives
Can ALWAYS be measured
Specify date by which it will be accomplished
Therefore a problem can arise when deadlines depend on each other. Example: Release Version 2.0 after completing beta testing AND complete beta tests prior to release of Version 2.0

3. Introduction
The general idea is to summarize and analyze data so that it is useful for decision-making
We do this by calculating “measures of central tendency” and by looking for relationships
(We will NOT cover formal tests of hypotheses)
Primary vs. secondary data sources
Data on uses (system) vs. data on users (people)

4. Data Data may be continuous or discrete
Just looking at the data often does not enable one to ascertain what is actually happening
Solution: Use appropriate descriptive statistics to summarize and present results
Another Data 

5. Analysis--Introduction
The BIG Question: What are you trying to discover or show?
From survey to report
Flow of information
Sample survey of California ISPs
Brief comparison of Excel & SPSS

6. Data Collection Instruments
Questionnaires & surveys
Transactions logs
Bills & invoices
Census forms & reports
Pre-packaged data sets

7. Issues in Research Design
Case study vs. statistical sample
What is the universe ? (uses, users, etc.)
Current political debate over “average tax cut” vs. “tax cut for the average family”
Is the sample representative ?
Volumes vs. titles in the library
Does correlation imply causality?
Do we need to identify the pathogen?
Controlling for outside factors

8. Sample Size How large a sample is needed?
The larger the sample the more accurate the results (unless the response rate becomes very low)
The larger the sample the more the cost/effort
Sample size does NOT depend on the size of the population
Rules of thumb
100 for 95% confidence, 5% tolerance, expected split
400 for 95% confidence, 5% tolerance, expected split
30 – 50 in each cell on n x m discrete classes
Exact formula (use with care):
Size = 0.25 * (certainty factor/acceptable error)^2
Where the certainty factor = 1.96 for 95%; for 99%

9. Sources of Error The respondent The investigator Sampling error
Change in the system itself
Coding & analysis
Other