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

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

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

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

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

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”

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.”

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

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

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

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

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)

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)

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