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 =
X
n
i
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.
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.
ii.
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