The document discusses the importance of statistics for managers to properly analyze and draw conclusions from data, as well as the concepts of populations, samples, parameters, statistics, descriptive statistics, inferential statistics, sources of data, types of data, and sampling methods. It provides examples of how to calculate descriptive statistics like the sample mean and how to perform inferential statistics like estimating population means and hypothesis testing using sample data.

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 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