1. BUSA 3110 Statistics for Business Spring 2014 – Week 1
Kim I. Melton, Ph.D.
Spring 2014

2. Business Statistics – Past…
Descriptive (a little time)
Summary statistics
Graphical insights
Inferential (lots of time)
Populations and Parameters
Samples and Statistics
Hypotheses, confidence levels, and p values
Generalizability from the sample to the population
Process Improvement (in between amount of time)
Determining if past performance might be useful for predicting
Understanding how changes in the process relate changes in characteristics of a product or service

3. About me

5. The Changing Face of Statistics
and
Business Analytics
Big Data
Descriptive,
Predictive,
Prescriptive
Vol43/43_2/dsi-dl43_2_feature.asp

6. Word Clouds Business Statistics Text   Business Analytics Text
(created at wordle.net)
Business Statistics Text 
 Business Analytics Text

9. No one should try to use data unless he has collected data.
Best efforts will not substitute for knowledge.
Information is not knowledge. Let’s not confuse the two.
Without questions, there is no learning.
There is no knowledge without theory.
Every theory is correct in its own world, but the problem is that the theory may not make contact with this world.
All theories are wrong—but some are still useful. (George Box’s reply)

10. Homework
Go to the public website: faculty.ung.edu/kmelton/busa3110.html
Complete the assignment listed there. [This does call for you to bring some notes with you to class about the videos and any questions that you have about the syllabus.]

11. Day 2 Syllabus questions Calorie confusion – sources of variation
Data analytics – and statistics
Big data – what data do you generate?

12. Day 2 Homework Posted on the public website
Faculty.ung.edu/kmelton/busa3110.html