Chapter 1: Statistics, Data, and Statistical Thinking

Chapter 1 Statistics, Data, and Statistical Thinking  The Science of Statistics Statistics – the science of data Collection Evaluation (classification, summary, organization and analysis) Interpretation  Types of Statistical Applications in Business Descriptive Statisti...

Chapter 1 Statistics, Data, and Statistical Thinking  The Science of Statistics Statistics – the science of data Collection Evaluation (classification, summary, organization and analysis) Interpretation  Types of Statistical Applications in Business Descriptive Statistics - describe collected data “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 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  Collecting Data Data Sources –Published source – books, journals, abstracts Primary vs. secondary –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 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  Collecting Data Question – a local TV station conducts exit polling during an election, selecting every 10th person who exits the polling station. Is this a random sample? No. Why? Before the first person is surveyed, there are only 10 subsets that can be selected from the whole population. Once the first person is surveyed, there is only 1 subset that can be selected from the whole population. The Role of Statistics in Managerial Decision Making Statistical literacy is necessary today to make informed decisions both at work and at home Requires statistical thinking to critically assess data and the inferences drawn from it Statistical thinking assists you in identifying research resulting from unethical statistical practices The Role of Statistics in Managerial Decision Making 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  Summary 2 types of statistical applications in business – Descriptive and Inferential 6 fundamental elements of statistics – population – experimental units – variable – sample – inference – measure of reliability  Summary 2 types of data – Quantitative and Qualitative 4 Data collection methods – published source – designed experiment – survey – observation  Summary Sources of Error in Survey Data – selection bias – non-response bias – measurement error