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Chapter 1 (web) introduction

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  • 1. Chapter 1: Data Collection• Why Statistics? A Manager Needs to Know Statistics in order to: – Properly present and describe information – Draw conclusions about populations based on sample information – Understand Statistical relationship (causality) – Improve processes – Obtain reliable forecasts• www.unlv.edu/faculty/nasser
  • 2. Key Concepts• A population (universe) is the collection of all items or things under consideration – A parameter is a summary measure that describes a characteristic of the entire population• A sample is a portion of the population selected for analysis – A statistic is a summary measure computed from a sample to describe a characteristic of the population
  • 3. Key Concepts, Continued• Descriptive statistics (art)-- Collecting, summarizing, and describing (presenting) data from a sample or a population• Inferential statistics – The process of using sample statistics to draw conclusion about the population parameters
  • 4. Example: Descriptive Statistics• Collect data – e.g., Survey• Present data – e.g., Tables and graphs• Characterize data ∑X i – e.g., Sample mean = n
  • 5. Example: Inferential Statistics• Estimation – e.g., Estimate the population mean weight using the sample mean weight• Hypothesis testing – e.g., Test the claim that the population mean weight is 120 pounds
  • 6. Sources of data• Before collection of data , a decision maker needs to: – Prepare a clear and concise statement of purpose – Develop a set of meaningful measurable specific objective – Determine the type of analyses needed – Determine what data is required
  • 7. Sources of Data, Continued• Primary Data Collection – Experimental Design – Conduct Survey – Observation (focus group)• Secondary Data Compilation/Collection – Mostly governmental or industrial, but also individual sources
  • 8. Types of Data• Random Variable – Values obtained are not controlled by the researcher (theoretically values differ from item to item)• Data from a RV are either: – Quantitative • Continuous (measuring) • Discrete (Counting) – Qualitative (categorical) • Nominal • Ordinal
  • 9. Types of Sampling Methods• Non-Probability Sampling -- Items included are chosen without regard to their probability of occurrence. i. Judgment ii. Quota iii. Chunk iv. Convenience• Probability Sampling – Items are chosen based on a known probability. Let N=size of the population and n=desired sample size i. With replacement -- Prob. of each item and any round =(1/N) ii. Without replacement -- Prob. of each item =(1/N), 1/(N-1), …1/ [N-(n-1)]
  • 10. Types of Probability Sampling• Items in the sample are chosen based on known probabilities Probability SamplesSimpleRandom Systematic Stratified Cluster
  • 11. Types of Probability Samples, Con’t• Simple Random Sample -- Every individual or item from the frame has an equal chance of being selected. In addition, any selected sample has the same chance of being selected as any other. – Samples obtained from table of random numbers or computer random number generators• Systematic Samples -- Divide frame of N individuals into groups of k individuals: k=N/n. Randomly select one individual from the 1st group. Then Select every kth individual thereafter
  • 12. Types of Probability Samples, Con’t• Stratified samples -- Divide population into subgroups (called strata) according to some common characteristic. A simple random sample is selected from each subgroup. Samples from subgroups are combined into one• Cluster Samples -- Population is divided into several “clusters,” each representative of the population. Then, a simple random sample of clusters is selected – All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique
  • 13. Evaluation of a Survey• What is the purpose of the survey?• Is the survey based on a probability sample?• Coverage error – appropriate frame?• Nonresponse error – follow up• Measurement error – good questions elicit good responses• Sampling error – always exists