1. Spring 2016 BUSA 3110 - Statistics for Business Module 1: Data
Kim I. Melton, Ph.D.

2. Syllabus Text, MyStatLab, JMP, D2L, MS Office
Accessing material D2L and MyStatLab
Software Availability: JMP and MS Office
Course Format
Grading
General expectations (especially deadlines, make-ups, extra credit, academic integrity, phones)
Inclement Weather

3. Data/Information/Knowledge/Wisdom
Doing things right
(Efficiency)
Doing the right things
(Effectiveness)
DATA
INFORMATION
KNOWLEDGE/ UNDERSTANDING
WISDOM
Symbols (raw values) that represent properties of objects/events
Describes; provides answers to who,
what, where, and when questions
Explains; provides answers to how to
and why questions
Evaluates knowledge/understanding;
deals with values; uses judgment;
answers what is best and why
Based on the work of Russell Ackoff. See “From Data to Wisdom” in Ackoff’s Best, pp , 1999.

4. Content Six “Modules” (Sets of Slides)
Data
Information
Knowledge
Wisdom
Content
Six “Modules” (Sets of Slides)
Data – What is it, Types of data, How can we use it
Summarizing Data – Visually and Quantitatively
Collecting “Good” Data
Inference Involving One Variable
Simple Linear Regression
Multiple Regression and Model Building

5. Grading MyStatLab Homework (16 points) MyStatLab Quizzes (16 points)
90 and above A
80 – 89 B
70 – 79 C
60 – 69 D
Below 60 F
MyStatLab Homework (16 points)
Drop the lowest two and average the rest [then take percent of 16]
MyStatLab Quizzes (16 points)
Average all [then take percent of 16]
Instructor Supplied Assignments (64 points)
Eight assignments each graded out of 8 [add them up]
Preparation / Participation (10 points)
Total earned/total available [then take percent of 16]
Pre-final grade = Add the points from each section
Final (0-16 points)
Two problems each out of eight point
Final Grade = Points from (HW + Quizzes + Preparation / Participation + 8 Highest Instructor Supplied Assignments)

6. Instructor Supplied Assignment Topics (Tentative List)
Fundamentals of using JMP
Summarizing Data
Collecting “Good” Data for Statistical Inference
Inference about One Variable
Equations, Graphs, Model Statements, Hypotheses
Simple Linear Regression
Multiple Regression and Testing Theories
Model Building and Selecting the “Best” Model

7. General Expectations
Learning is not a divided responsibility (I teach, you learn)—learning is a joint responsibility (we learn together)
My “hot buttons”
Timeliness
Ethical behavior
Professional orientation toward learning
This includes putting phones away and engaging in class
Recognition that “true” learning involves more than getting the right answer

8. What is/are Statistics?
Statistics vs. statistics
Statistics vs. Math

9. How Does Statistics (as a field of study) Apply to these Videos
How Does Statistics (as a field of study) Apply to these Videos? … And to what industries?
Videos
Think
Business Analytics and Optimization
Turning Data into Insight
Business Analytics:  Data Trends let Businesses Spot New Opportunities
THINK:  A Film about Making the World Work Better

10. How (and Why) is the Field of Statistics Changing?
Source:

11. Analytics

12. Impact of Analytics on the Way we Think about ___
Statistics?
Evolution vs. revolution
Improvement vs. innovation
1st order change vs. 2nd order change
change in how we do something (1st order) vs. change in what we do (2nd order)
Paradigm shift…makes us go back to the most basic assumptions

13. Based on the HBR Article
Analytics 2.0
Analytics 3.0
What is this?
Examples from your life:
What is this?
Data aligned with analytics 3.0 that you are providing companies:

14. A Word about Deadlines (MyStatLab and D2L)
Deadlines are set to:
Allow you time to see assignments well before the due date
Allow you time to complete the assignments after the material is covered
Provide you with as much time as possible prior to when I will start grading
Therefore, I will use early morning deadlines rather than late night deadlines (giving you the option of the overnight hours to work)
Remember, you can submit assignments before the deadline

15. Why Start with the LAST Chapter in the Book? (Chapters 24)
This is a second course in statistics. This chapter lets you reflect on the tools/ techniques from the first course…and sets the stage for this course.
CONTEXT

16. 24.8 The Data Mining Process
QTM1310/ Sharpe
24.8 The Data Mining Process
(and also applies to most any data analysis in practice)

17. 24.8 The Data Mining Process (and data analysis in practice)
QTM1310/ Sharpe
24.8 The Data Mining Process
(and data analysis in practice)
The process must start with the Business Understanding phase.
Data Understanding is central to the entire data mining project – it is crucial to understand the data warehouse, what it contains, and what limitations are present.
Once variables are selected and the response variable has been agreed upon, the Data Preparation phase begins.
Following preparation is the Data Modeling phase. The more knowledge of the data and the variables that goes into the model, the higher the chances of success for the entire project.
Finally, if the model seems to give business insight, it’s time for the Deployment phase – just keep in mind that the business environment changes rapidly, so models can become stale quickly.

18. 24.4 Data Mining Myths Myth 1: Find answers to unasked questions.
QTM1310/ Sharpe
24.4 Data Mining Myths
Myth 1: Find answers to unasked questions.
Myth 2: Automatically monitor a database for interesting patterns.
Myth 3: Eliminate the need to understand the business.
Myth 4: Eliminate the need to collect good data.
Myth 5: Eliminate the need to good data analysis skill.

19. 24.5 Successful Data Mining
QTM1310/ Sharpe
24.5 Successful Data Mining
The first step is to have a well-defined business problem, which can help you avoid going down a lot of blind paths.
Typically, 65% to 90% of the time is spent in data preparation – investigating missing values, correcting wrong entries, reconciling data definitions, or creating new variables from old ones.

20. QTM1310/ Sharpe
Be sure that the question to be answers is specific. A goal as vague as “improving the business” is not likely to be successful.
Be sure that the data have the potential to answer the question. Check the variables to see whether a model can reasonably be built to predict the response.
Be aware of overfitting the data. Make sure you validate the model on a test set.
Make sure that the data are ready to use in the data mining model. Missing values, incorrect entries, and different time scales are all challenges that need to be overcome.
Don’t try it alone. Data mining projects require a variety of skills and a lot of work. Assemble the right team of people. 20

21. What do we mean by “good data”?
Considerations when collecting data
Considerations when evaluating claims based on data

22. Characteristics of “Good” Data
Accuracy of measurement
Precision of measurement
Uses an appropriate type data (level of measurement)
Nominal, Ordinal, Interval, Ratio
Interval and Ratio are often grouped as continuous or quantitative
Aligns with the characteristic of interest
Different numbers reflect differences in the items measured (rather than an inability to measure consistently)
Measurement is a yardstick for “how we are doing” rather than the “mission”
Parking Space Reserved for Drive-Thru

23. Putting Data in Context (5 W’s and H)
Who does the data describe (doesn’t have to be people)
What characteristics are recorded (variables of interest)
Why are we collecting data (purpose, guiding questions,…)
How were the data collected (theory-wise and physically)
Sampling, convenience, primary or secondary data, training for data collection
Operational definitions will describe what is “measured”, how the measurements are taken (getting to the level of measurement level/modeling type and method of measurement), and provide a way that two people looking at the same item would come to the same conclusion about the characteristic.
When were the data collected (date/time, across time, …)
Where were the data collected (geographic, point in process, source…)

24. Describe, Explain, Understand, Predict, Prescribe
What were our sales for the month? (describing)
How does this compare to the same month last year? (still describing)
What’s changed that might account for the differences? (moves toward explaining)
Why have sales changed? (starts to move from explaining to understanding)
What will sales be in the future? (predicting and/or prescribing)

25. Data for Decision Making
Major issues
Purpose (descriptive, predictive, prescriptive)
Measurement Level (quantitative/qualitative, nominal, ordinal, interval, ratio)
Variable choice and definition
Sources of variation (population, across time, process)
Methods of accessing (primary, secondary)
Choice of observations (random, convenience, rational)
External influences (ethical and practical)

26. Variable Choice and Measurement Level (Modeling Type)
Identify the Level/Type
Nominal (Qualitative, Categorical)
Ordinal (Qualitative, Categorical, Logical to Order the Categories)
Interval (Quantitative, Differences have consistent meaning)
Ratio (Quantitative, Differences and Ratios have meaning) NOTE: JMP combines Interval and Ratio into Continuous
Major
Grade in a course
Job title
Year in school (Freshman,…, Senior)
Price of a gallon of regular gas
Salary
Rank of your favorite college team
Size of a house
Gender
Level of agreement (1, 2, …, 9, 10 where higher numbers relate to stronger agreement)

27. 1 3 2 4 Lists of Most Stolen Vehicles Ford F-250 crew 4WD
Chevrolet Silverado 1500 crew
Chevrolet Avalanche 1500
GMC Sierra 1500 crew
Ford F-350 crew 4WD
Cadillac Escalade 4WD
Chevrolet Suburban 1500
GMC Sierra 1500 extended cab
GMC Yukon
Chevrolet Tahoe
Toyota Camry/Solara
Toyota Corolla
Chevrolet Impala
Dodge Charger
Chevrolet Malibu
Ford Fusion
Nissan Altima
Ford Focus
Chevrolet Cobalt
Honda Civic 1 3
1994 Honda Accord
1998 Honda Civic
2006 Ford Full Size Pickup
1991 Toyota Camry
2000 Dodge Caravan
1994 Acura Integra
1999 Chevrolet Full Size Pickup
2004 Dodge Full Size Pickup
2002 Ford Explorer
1994 Nissan Sentra
Dodge Charger
Pontiac G6
Chevrolet Impala
CHRYSLER 300
Infiniti FX35
Mitsubishi Galant
Chrysler Sebring
Lexus SC
Dodge Avenger
Kia Rio
Highway Loss Data Institute (Insurance claims $)
National Insurance Crime Bureau (thefts reported to law enforcement)
National Highway Traffic Safety Adm. (FBI data) 2 4

28. http://www. realclearpolitics
(accessed 1/19/16)

29. Cross Sectional vs. Time Series
(accessed 1/19/16)

30. Other Issues in Data Collection
External influences (practical and ethical)
Practical (time, money, access)
Ethical
policies that can interfere with collecting good data:
evaluation systems that look at components separately
reward systems
quotas and arbitrary goals
fiscal year budgets
Other issues to cover in Chapter 8
Methods of accessing (primary, secondary; survey, experiment, observational)
Choice of observations (random, convenience, rational)