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Chapter 1

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University of Guelph
STAT 2060
Peter Kim

Chapter 1: Statistics, Data, and Statistical Thinking 1.1 The Science of Statistics Statistics-science of using data to describe characteristics of a group of objects & to make inferences about the group. (Involves collecting, classifying, summarizing, organizing, analysing, & interpreting info & data) 1.2 Types of Statistical Applications in Business Descriptive Statistics- uses numbers & graphs to look at patterns in data set (summarizes info & present s in convenient form) Four Elements of Descriptive Statistical Problems: 1. Identify population or sample (collection of experimental units) 2. Identify variable(s) 3. Collect data 4. Describe data Inferential Statistics- uses sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data Five elements of Inferential Statistical Problems: 1. Identify population (collection of all experimental units) 2. Identify variable(s) 3. Collect sample data (subset of population) 4. Inference about population based on sample 5. Measure of reliability for inference *Examples where statistics are used in business: data mining, e-commerce, forecasting, investment analysis, marketing, pricing strategies 1.3 Fundamental Elements of Statistics Unit- objects on which measurements are recorded Population- set of all units of interest(all employed workers in the USA) Parameter-numerical characteristics of a population Variable- characteristic or property of an individual experimental unit (age, gender, income, etc.) Measurement- process used to assign numbers to variables of individual population units (scale ratings) Census- measuring a variable for every experimental unit of a population Sample- subset of the units of a population (any set of output produced by a process) Estimator- numerical characteristics of the sample Statistical Inference- estimate, prediction or some other generalization about a population based on information containe
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