1. Understanding Data Data and Analysis

2. Agenda A Modicum of Probability & Statistics Review in GBA 412 The Interview Riddle Understanding Data Types A Preview things to come …

3. Probability What is a Probability? Dependence and Conditioning Expected Values and Covariances Probability Densities and Distributions Sample measures Mean, Median & Mode Standard Deviation and Variance Correlation

4. Statistics Central Limit Theorem Hypothesis Testing P Values Confidence Intervals Statistical Significance Estimation Point Estimation Interval Estimation

5. The Interview Riddle Reading between the lines…

6. Solving the Riddle… Frame the Problem Construct a Model Methods? Data? Analysis Results

7. Takeaways… Data needs to be constructed Data isn’t obvious Data isn’t always numerical Absence of data is also data Obtaining relevant data may need expertise Data is a function of human endeavor

8. Data Types Types based on “purpose” and “source” Primary Data Data obtained by you for your own particular needs Examples: Data from Lab Experiments Survey conducted by you Secondary Data Data collected by someone else for some other purpose Examples: Census Data Syndicated Data (Scanner/Stock Market)

9. Types of Data Types based on “action” Survey Data Data based on what people said Examples: How likely are you to buy? How satisfied are you with your purchase? Behavioral Data Data reflecting what people did Examples: Supermarket Scanner Data Stock Market Data

10. Types of Data Types based on “mathemetical properties” Four types: Nominal Data Ordinal Data Interval (Scale) Data Ratio Data Data often determines type of models and analysis to be used

11. Nominal Data Nominal data is data that can be used only for identification purposes It has no mathematical meaning Consequently, mathematical operators such as addition, subtraction, division etc. cannot be applied Analysis tools are usually those that aggregate, count or otherwise describe such data. Easiest data type to collect Examples: Social Security Numbers, Phone Numbers

12. Rank Order Data Data that reveals the order of a set of items Apart from the order there is no additional mathematical information in the numbers Again, mathematical operations do not apply Analysis tools include descriptive methods and certain specific causal techniques Examples: Business School Ranks Race Positions

13. Interval Scale Data Data that seeks to put information on a defined interval scale The data has limited mathematical properties. Differences in the numeric values have meaning but ratios do not. i.e. addition/subtraction are OK but division/multiplication are not Analysis tools include descriptive, relational and causal methods. Examples: Satisfaction Survey Answers Mutual Fund Ratings

14. Ratio Scale Data The most flexible data type The continuous nature of the data allow all mathematical manipulations Analysis tools are varied and include numerous descriptive and causal methods Probably the most difficult data to collect Examples: Stock Price Income

15. Types of Data Types based on “unit(s) of analysis” Cross-sectional Data Data in which each unit of analysis only occurs once Examples: Satisfaction Survey across people Sales/Prices of Pepsi across Markets Time Series Data Data for one entity but across many time periods Examples: US GDP for the past 100 years Dow over time Panel Data Combination of cross-sectional and time series data where many entities have data over many time periods Examples: Stock prices of various firms over time Market shares of competitors in many markets over time

16. What’s to come … GBA 412 Lab Introduction to probability and statistics (Week I and II) GBA 412 Basic Data Analysis Pictures and Tables