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

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

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

Example: Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Characterize data e.g., Sample mean =

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

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

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

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

Types of Sampling Methods Non-Probability Sampling -- Items included are chosen without regard to their probability of occurrence. Judgment Quota Chunk Convenience Probability Sampling – Items are chosen based on a known probability. Let N=size of the population and n=desired sample size With replacement -- Prob. of each item and any round =(1/N) Without replacement -- Prob. of each item =(1/N), 1/(N-1), …1/[N-(n-1)]

Types of Probability Sampling Items in the sample are chosen based on known probabilities Probability Samples Simple Random Systematic Stratified Cluster

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

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

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