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

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
e.g., Sample mean =

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.
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)]

10. Types of Probability Sampling
Items in the sample are chosen based on known probabilities
Probability Samples
Simple
Random
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