1. BUSA 3110 Statistics for Business Spring 2014 – Week 2
Kim I. Melton, Ph.D.
Spring 2014

2. Homework
Still posted at the public website… Faculty.ung.edu/kmelton/busa3110.html … but getting closer to moving to D2L for posting assignments.

3. Your List of Major Topics from Readings (Chapters 1 and 2.1)

4. Concepts from Chapters 1 and 2.1
Descriptive Statistics
Measures of location (central tendency)
Spread (variability)
(and indirectly) shape
Inferential Statistics
Population and parameter
Sample and statistic
Confidence level and significance level
[missing] Statistics for Analyzing Processes
Chapter 2, Section 1
Levels of Measurement

5. Key Terms Population Parameter Sample Statistic Theory Hypothesis
Significance (sufficient evidence)
Stability
Levels of Measurement (nominal, ordinal, interval, and ratio scale)

6. Underlying Concepts/Terms
Variables
Data
Operational definitions
Extending conclusions beyond the current dataset
Theories and Hypotheses
From sample to population
Variability (and sources of variation)
From current data to a different time or place

7. Data There is no such thing as “objective data.” Someone decides:
What data to collect
When to collect the data
How to collect the data
How to define the characteristic of interest
Some data are more objective than other data.
Examples:
Write a one page paper describing _____.
Count the f’s
Count the pages
What constitutes “most” of the time?

8. What vehicle is the “most stolen?”
What does statistics have to do with answering this question?
Hint: Look at four different lists that claim to provide an answer…

9. 1 3 2 4 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 2 4

10. Most Stolen Cars
Highway Loss Data Institute - Vehicles with the highest theft claim rates (2012)
Based on reported claims from insurance (and do not distinguish between contents and vehicle thefts)
National Insurance Crime Bureau – Most stolen vehicles (2011)
Based on vehicle thefts reported to law enforcement
National Highway Traffic Safety Administration – Most stolen vehicles (2010)
Based on FBI data on reported vehicle thefts
Based on FBI data on reported vehicle thefts per 1000 produced

11. Operational Definitions
Tells what to measure
How to measure
When to measure
How to interpret the result
Relating to the stolen car example:
Counting “thefts” of “cars”
When is something a theft? What is a car?
How will we identify “thefts” and at what level of detail?
Over what period of time (for the thefts) and when will we collect the data
Are we looking at raw counts or some kind of relative measure?

12. Three uses for statistics (Traditional)
Describe (Descriptive Statistics)
Summarizes data
Graphically
Through formulas and tables
Infer (Inferential Statistics)
Use data from a small number of observations to draw conclusions about the larger group
Improvement (Process Studies)
Use data from past experience to help predict expected outcomes at a different time or place or to direct action to influence future outcomes

13. Three uses for statistics (Evolving)
Descriptive
Includes current descriptive and inferential statistics
Looks at past and current performance to “describe”
Predictive
Looks at past and current performance with a goal of predicting future performance (i.e., to be able to “explain”)
Addresses what “if questions”
Prescriptive
Uses quantitative models to assess how to operate in order to achieve some objective within constraints (and may include deterministic and probabilistic aspects)

14. Descriptive Statistics
Summary measures for some situation
May be meant to provide general information about that situation
May be intended (under appropriate conditions) to be used to generalize to some larger group.
Occasionally (and inappropriately), used to say something about what to expect in some other time or place.

15. Inferential Statistics (in layman’s terms)
You have:
Large group of interest
A small number of “representative” observations from that group
You want:
To draw some conclusion about a characteristic of the large group based on what you observe from the observations available
You know:
That your conclusion could be wrong, but you want to be “close.”

16. Inferential Statistics (with statistical terms)
We have
A population of interest
A random sample of observations from the population
You want:
To draw some conclusion about a parameter of the population based on what you observe from the calculation of a statistic from the sample
You recognize that there will be sampling error in your estimate and want to provide a level of confidence for any results that you state.

17. Populations and Parameters Samples and Statistics
The collection of all items of interest OR more specifically:
The measurements that would be obtained from evaluating all items of interest
Parameter
A summary measure obtained by using data from all elements of the population
Usually identified with a Greek letter (m, s, p, b0)
Sample
A subset of the population (the items actually examined) OR more specifically:
The measurements that are obtained from the subset of the population
Statistic
A summary measure obtained by using the data obtained from the sample
Usually identified with traditional English letters ( , s, p, b0)

18. Parameters and Statistics
Location Mean Median Mode
 (read mu) no uniform symbol no uniform symbol
(read X Bar) no uniform symbol no uniform symbol
Spread Range Variance Standard Deviation
R 2 (read sigma squared)  (read sigma)
R s2 s
Proportions
p (read pi) or p
(read p hat)

19. Statistics for Process Studies (we’ll come back to this later)
Two issues arise:
Changes can occur in an on-going process while you are collecting data—i.e., you don’t know if all of your data is coming from the same population
Although describing past output may be useful, this is descriptive (history). You really want to be able to know what to expect in the future—i.e., you aren’t trying to make an inference about the process as it existed while you were collecting data.

20. Levels of Measurement
Nominal – Qualitative; categorical; order has no meaning
Ordinal – Qualitative; categorical; order has meaning; distance between categories does not
Interval – Quantitative; distance has meaning; zero is “arbitrary”
Ratio – Quantitative; distance has meaning; zero equates to “none of”

21. Examples Major Width of parking place Brand of soft drink preferred
Year in school (Freshman,…, Senior)
Year of birth
Snowfall amount
Time to complete an assignment
Salary
Grade (A,B,C,D,F)
Grade on test (0 to 100)
Level of agreement (strongly disagree, … strongly agree)
Gender
Job title
Make of car

22. Calculations and Levels of Measurement
For the results of addition, subtraction, multiplication, and division to have meaning, data needs to be at least interval in scale.
For the results of calculations to be useful in prediction/estimation, certain conditions must exist in terms of how the data are collected.

23. Graphical Tools Bar charts Scatter diagrams Run charts Pie charts
stacked
unstacked
Scatter diagrams
Run charts
Pie charts
Histograms

24. Matching Methods to Data
Bar Charts
Typically for Nominal or Ordinal Data
Pie Charts
Nominal or Ordinal (but better for Nominal)
Run Charts
Interval or Ratio
Histograms
Interval or Ratio (but the order of the data is lost)
Scatter diagrams
Usually two Interval or Ratio scale variables

25. Scenario We need to “build a schedule.”
One part of this is determining demand for courses
Consider real data from NGCSU
Variables:
Headcount and credit hour production for the Dept/School of Business each Fall (01-12)
Number of majors each Spring (07-13)
Sample of student total earned hours for a single semester

26. Questions
Building schedules involves forecasting demand. Can we use headcount (of majors) to estimate credit hours?
Would we expect the same relationship between headcount and credit hours in the English Department?
How is enrollment changing over time? Are we growing?
What is happening to enrollment by campus?
How are our students distributed across majors?
How are students distributed across earned hours distributed?

27. Can we use headcount to estimate credit hours?
Scatter Diagram
Students
Credit Hrs.
703
6167
751
6317
741
6779
719
6629
801
6777
837
6426
895
7017
925
7098
904
7002
891
7134
937
7341
987
8174

28. How is enrollment changing over time? Are we growing?
Run Chart (aka Excel Line Chart)

29. Distortion of Graphical Display

30. What is happening with enrollment on each campus?
Bar Chart (aka Excel Column Chart)

31. Would a Run Chart be easier to read?

32. How are our students distributed across majors?
Pie Chart

33. How are our students distributed across majors?
Pie Chart But would a Bar Chart be better?

34. Is a “Snap Shot” the best we can do?

35. How are earned hours distributed?
Histogram (best made using Data Analysis Tools)
Relative frequency histogram (Column chart)