1. Statistics, Data, and Statistical Thinking
Chapter 1
Statistics, Data, and Statistical Thinking

2. The Science of Statistics
Statistics – the science that deals with the collection, classification, analysis, and interpretation of information or data
Collection
Evaluation (classification, summary, organization and analysis)
Interpretation

3. Collecting Data Data Sources
Published source – books, journals, abstracts
The Wall Street Journal, The Sporting News
Designed Experiment
Often used for gathering information about an intervention
Survey
Data gathered through questions from a sample of people
Observational Study
Data gathered through observation, no interaction with units

4. Collecting Data Common Sources of Error in Survey Data
Selection bias – exclusion of a subset of the population of interest prior to sampling
Non-response bias – introduced when responses are not gotten from all sample members
Measurement error – inaccuracy in recorded data. Can be due to survey design, interviewer impact, or a transcription error

5. Collecting Data Sampling
Sampling is necessary if inferential statistics are to be used
Samples need to be representative
Reflect population of interest
Random Sampling
Most common sampling method to ensure sample is representative
Ensures that each subset of fixed size is equally likely to be selected

6. Types of Statistical Applications in Business
Descriptive Statistics - describe collected data, utilize numerical and graphical methods to present the information
“51.4% of all credit card purchases in the 1st quarter of 2003 were made with a Visa Card”
“The average Return-to-Pay Ratio of Financial Industry CEOs (2003) was 24.63”

7. Types of Statistical Applications in Business
Inferential Statistics - make generalizations about a group based on a subset (sample) of that group
“Services Industry CEOs are underpaid relative to CEOs in Telecommunications.”

8. Fundamental Elements of Statistics
Experimental Unit – object of interest
example – graduating senior
Population – the set of units we are interested in learning about
example – all 1450 graduating seniors at “State U”
Variable – characteristic of a single experimental unit
example – age at graduation

9. Fundamental Elements of Statistics
Sample – subset of population
example – 100 graduating seniors at “State U”
Statistical Inference – generalization about a population based on sample data
example – The average age at graduation is 21.9 (based on sample of 100)
Measure of reliability – statement about the uncertainty associated with an inference

10. Fundamental Elements of Statistics
Elements of Descriptive Statistical Problems
Population/sample of interest
Investigative variables
Numerical summary tools (charts, graphs, tables)
Pattern identification in data

11. Fundamental Elements of Statistics
Elements of Inferential Statistical Problems
Population of interest
Investigative variables
Sample taken from population
Inference about population based on sample data
Reliability measure for the inference

12. Types of Data Quantitative Data
Measured on a naturally occurring numerical scale
Equal intervals along scale (allows for meaningful mathematical calculations)
Data with absolute zero (zero means no value) is ratio data (bank balance, grade)
Data with relative zero (zero has value) is interval data (temperature)

13. Types of Data Qualitative Data Measured by classification only
Non-numerical in nature
Meaningfully ordered categories identify ordinal data (best to worst ranking, age categories)
Categories without a meaningful order identify nominal data (political affiliation, industry classification, ethnic/cultural groups)

14. Types of Data
Different statistical techniques used for quantitative and qualitative data
Qualitative and Quantitative data can be used together in some techniques
Quantitative data can be transformed into Qualitative data through category creation
Qualitative data cannot be meaningfully transformed into Quantitative data