1. ISQS 5345-002 - STATISTICAL CONCEPTS FOR BUSINESS AND MANAGEMENT

2. What is Statistics?

3. A * way of understanding data. * Not the way.

4. What is Data?

5. Transform The Understanding Process

6. Why study statistics?

9. To Summarize Stat is A way of understanding data Data is anything perceivable Data is converted to information for understanding Stat is a language of business Stat is a series of if…then statements Human brains do not naturally think statistically

10. Age Height Income Hair Color Favorite Food Attributes Thing 25 Years 1.5 M $40,000 Brown Cheesecake Values Elementary Units Variable

11. Age Height Income Hair Color Favorite Food Attributes Thing 45Years 1.7 M $70,000 Gray Cheesecake Values Elementary Units Variable

12. Age Height Income Hair Color Favorite Food Attributes Thing 25Years 1.5 M $40,000 Brown Cheesecake Values Elementary Units Variable

13. A dataset is a collection of values of attributes of many things

14. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Univariate data set: One variable measured for each elementary unit – e.g., Sales for the top 30 computer companies. – Can do: Typical summary, diversity, special features Bivariate data set: Two variables – e.g., Sales and # Employees for top 30 computer firms – Can also do: relationship, prediction Multivariate data set: Three or more variables – e.g., Sales, # Employees, Inventories, Profits, … – Can also do: predict one from all other variables How Many Variables?

15. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Numbers or Categories? Quantitative Variable: Meaningful numbers – e.g., Sales, # Employees – Can add, rank, count Qualitative Variable: Categories – Ordinal Variable: Categories with meaningful ordering e.g., Bond rating (AA, A, B, …), Diamonds (VSI, SI, …) Can rank, count – Nominal Variable: categories without meaningful ordering e.g., State, Type of business, Field of study Can count

16. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Time-Series or Cross-Sectional? Time-Series Data: Data values recorded in meaningful sequence – Elementary units might be days or quarters or years – e.g., Daily Dow-Jones stock market average close for the past 90 days – e.g., Your firm’s quarterly sales over the past 5 years Cross-Sectional Data: No meaningful sequence – e.g., Sales of 30 companies – e.g., Productivity of each sales division – Easier than time series!

17. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Example FirmSalesIndustry GroupS&P Rating IBM66,346Office EquipmentA Exxon59,023FuelA- GE40,482ConglomeratesA+ AT&T34,357TelecommunicationsA-

18. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Example (continued) FirmSalesIndustry GroupS&P Rating IBM66,346Office EquipmentA Exxon59,023FuelA- GE40,482ConglomeratesA+ AT&T34,357TelecommunicationsA- Multivariate Data (3 variables) Elementary units Quantitative variable Nominal Qualitative variable Ordinal Qualitative variable Cross- Sectional

19. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Example YearSmall Business Administration Budget ($ Millions) 1995677 1996873 1997333 1998-77 199957 2000287

20. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Example YearSmall Business Administration Budget ($ Millions) Elementary unit defined by “year” Quantitative data Time series 1995677 1996873 1997333 1998-77 199957 2000287

21. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Stock Market – Time Series S&P Stock Index, monthly since 1925 0 200 400 600 800 1,000 1,200 1,400 1,600 192019301940195019601970198019902000 Year S&P stock market index

22. Irwin/McGraw-Hill© Andrew F. Siegel, 2003 Sources of Data Primary Data When you control the design and data collection Production data from your factory Your firm’s marketing studies Secondary Data When you use data previously collected by others for their own purposes Government data: economics and demographics Media reports – TV, newspapers, Internet Companies that specialize in gathering data