1. Business Statistics
Chapter 1
Introduction
to Statistics
By Ken Black

2. Learning Objectives Define statistics
Become aware of a wide range of applications of statistics in business
Differentiate between descriptive and inferential statistics
Classify numbers by level of data and understand why doing so is important 2 2

3. Statistics in Business
Best Way to Market
Stress on the Job
Financial Decisions
How is the Economy Doing
The Impact of Technology at Work 7 8

4. Examples of data in functional areas
accounting - cost of goods, salary expense, depreciation, utility costs, taxes, equipment inventory, etc.
finance - World bank bond rates, number of failed savings and loans, measured risk of common stocks, stock dividends, foreign exchange rate, liquidity rates for a single-family, etc.
human resources - salaries, size of engineering staff, years experience, age of employees, years of education, etc.
marketing - number of units sold, dollar sales volume, forecast sales, size of sales force, market share, measurement of consumer motivation, measurement of consumer frustration, measurement of brand preference, attitude measurement, measurement of consumer risk, etc.
information systems - c.p.u. time, size of memory, number of work stations, storage capacity, percent of professionals who are connected to a computer network, dollar assets of company computing, number of “hits” on the Internet, time spent on the Internet per day, percentage of people who use the Internet, retail dollars spent in e-commerce, etc.
production - number of production runs per day, weight of a product; assembly time, number of defects per run, temperature in the plant, amount of inventory, turnaround time, etc.
management - measurement of union participation, measurement of employer support, measurement of tendency to control, number of subordinates reporting to a manager, measurement of leadership style, etc.

5. Examples of data in business industries
manufacturing - size of punched hole, number of rejects, amount of inventory, amount of production, number of production workers, etc.
insurance - number of claims per month, average amount of life insurance per family head, life expectancy, cost of repairs for major auto collision, average medical costs incurred for a single female over 45 years of age, etc.
travel - cost of airfare, number of miles traveled for ground transported vacations, number of nights away from home, size of traveling party, amount spent per day on nonlodging, etc.
retailing - inventory turnover ratio, sales volume, size of sales force, number of competitors within 2 miles of retail outlet, area of store, number of sales people, etc.
communications - cost per minute, number of phones per office, miles of cable per customer headquarters, minutes per day of long distance usage, number of operators, time between calls, etc.
computing - age of company hardware, cost of software, number of CAD/CAM stations, age of computer operators, measure to evaluate competing software packages, size of data base, etc.
agriculture - number of farms per county, farm income, number of acres of corn per farm, wholesale price of a gallon of milk, number of livestock, grain storage capacity, etc.
banking - size of deposit, number of failed banks, amount loaned to foreign banks, number of tellers per drive-in facility, average amount of withdrawal from automatic teller machine, federal reserve discount rate, etc.
healthcare - number of patients per physician per day, average cost of hospital stay, average daily census of hospital, time spent waiting to see a physician, patient satisfaction, number of blood tests done per week.

6. What is Statistics?
Science of gathering, analyzing, interpreting, and presenting data
Branch of mathematics
Course of study
Facts and figures
A death
Measurement taken on a sample
Type of distribution being used to analyze data 8 11

7. Population Versus Sample
Population — the whole
a collection of persons, objects, or items under study
Census — gathering data from the entire population
Sample — a portion of the whole
a subset of the population 9 12

8. Population 13

9. Population and Census Data
Identifier
Color
MPG
RD1
Red 12
RD2 10
RD3 13
RD4
RD5
BL1
Blue 27
BL2 24
GR1
Green 35
GR2
GY1
Gray 15
GY2 18
GY3 17 11 14

10. Sample and Sample Data Identifier Color MPG RD2 Red 10 RD5 13 GR1
Green 35
GY2
Gray 18 15

11. Descriptive vs. Inferential Statistics
Descriptive Statistics — using data gathered on a group to describe or reach conclusions about that same group only eg. Average score
Graphs, tables and charts that display data so that they are easier to understand are examples of descriptive statistics.
Inferential Statistics (inductive statistics)— using sample data to reach conclusions about the population from which the sample was taken eg. Pharmaceutical research
The process of estimation of any parameter is referred as statistical inference. 13 16

12. Parameter vs. Statistic
Parameter — descriptive measure of the population
Usually represented by Greek letters
Statistic — descriptive measure of a sample
Usually represented by Roman letters 14 17

13. Symbols for Population Parameters15 18

14. Symbols for Sample Statistics16 19

15. Process of Inferential Statistics17 20

16. Levels of Data Measurement
Nominal — Lowest level of measurement
Ordinal
Interval
Ratio — Highest level of measurement 18 21

17. Nominal Level Data Numbers are used to classify or categorize
Example: Employment Classification
1 for Educator
2 for Construction Worker
3 for Manufacturing Worker
Example: Ethnicity
1 for African-American
2 for Anglo-American
3 for Hispanic-American 19 22

18. Ordinal Level Data Numbers are used to indicate rank or order
Relative magnitude of numbers is meaningful
Differences between numbers are not comparable
Example: Ranking productivity of employees
Example: Taste test ranking of three brands of soft drink
Example: Position within an organization
1 for President
2 for Vice President
3 for Plant Manager
4 for Department Supervisor
5 for Employee 20 23

19. Ordinal Data
Faculty and staff should receive preferential treatment for parking space. 1 2 3 4 5
Strongly
Agree
Disagree
Neutral

20. Interval Level Data Distances between consecutive integers are equal
Relative magnitude of numbers is meaningful
Differences between numbers are comparable
Location of origin, zero, is arbitrary
Vertical intercept of unit of measure transform function is not zero
Example: Fahrenheit Temperature
Example: Calendar Time 22 26

21. Interval Level Data
Like the others, you can remember the key points of an “interval scale” pretty easily.  ”Interval” itself means “space in between,” which is the important thing to remember–interval scales not only tell us about order, but also about the value between each item.
Here’s the problem with interval scales: they don’t have a “true zero.”  For example, there is no such thing as “no temperature.”  Without a true zero, it is impossible to compute ratios.  With interval data, we can add and subtract, but cannot multiply or divide. Ok, consider this: 10 degrees + 10 degrees = 20 degrees.  No problem there.  20 degrees is not twice as hot as 10 degrees, however, because there is no such thing as “no temperature” when it comes to the Celsius scale.  I hope that makes sense.  Bottom line, interval scales are great, but we cannot calculate ratios, which brings us to our last measurement scale…

22. Ratio Level Data Highest level of data measurement
Relative magnitude of numbers is meaningful
Differences between numbers are comparable
Location of origin, zero, is absolute (natural)
Vertical intercept of unit of measure transform function is zero.
These variables can be meaningfully added, subtracted, multiplied, divided (ratios)
Examples: Height, Weight, and Volume
Example: Monetary Variables, such as Profit and Loss, Revenues, and Expenses
Example: Financial ratios, such as P/E Ratio, Inventory Turnover, and Quick Ratio. 23 27

23. Usage Potential of Various Levels of Data
Ratio
Interval
Ordinal
Nominal 24 28

24. Data Level, Operations, and Statistical Methods
Nominal
Ordinal
Interval
Ratio
Meaningful Operations
Classifying and Counting
All of the above plus Ranking
All of the above plus Addition, Subtraction, Multiplication, and Division
All of the above
Statistical
Methods
Nonparametric
Parametric 25 29

25. Thank U