This chapter introduces fundamental statistical concepts for managers. It defines key terms like population, sample, and parameter and discusses descriptive and inferential statistics. The chapter outlines different data collection methods and sampling techniques, including probability and non-probability samples. It also covers data types, levels of measurement, evaluating survey quality, and sources of survey error. The goal is to explain why understanding statistics is important for managers to analyze data and make informed decisions.

1. Statistics for Managers
Using Microsoft® Excel
4th Edition
Chapter 1
Introduction and Data Collection
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc.
Chap 1-1

2. Chapter Goals
After completing this chapter, you should be
able to:

Explain key definitions:
♦ Population vs. Sample
♦ Primary vs. Secondary Data
♦ Parameter vs. Statistic
♦ Descriptive vs. Inferential Statistics

Describe key data collection methods

Describe different sampling methods


Probability Samples vs. Non-probability Samples
Select a random sample using a random numbers table
Identify types of data and levels of measurement
Statistics for Managers Using
 Describe
Microsoft Excel, the © 2004 types of survey error
4e different
Chap 1-2
Prentice-Hall, Inc.


3. Why a Manager Needs to
Know about Statistics
To know how to:

properly present information

draw conclusions about populations based
on sample information

improve processes

obtain reliable forecasts
Statistics for Managers Using
Microsoft Excel, 4e © 2004
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Chap 1-3

4. Key Definitions

A population (universe) is the collection of all
items or things under consideration

A sample is a portion of the population
selected for analysis

A parameter is a summary measure that
describes a characteristic of the population
A statistic is a summary measure computed
from a sample to describe a characteristic of
Statistics for Managers Using
the population

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Chap 1-4

5. Population vs. Sample
Population
a b
Sample
cd
b
ef gh i jk l m n
o p q rs t u v w
x y
z
Measures used to describe
the population are Using
Statistics for Managerscalled
parameters
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c
gi
o
n
r
u
y
Measures computed from
sample data are called
statistics
Chap 1-5

6. Two Branches of Statistics

Descriptive statistics


Collecting, summarizing, and describing data
Inferential statistics

Drawing conclusions and/or making decisions
concerning a population based only on sample
data
Statistics for Managers Using
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Chap 1-6

7. Descriptive Statistics

Collect data


Present data


e.g., Survey
e.g., Tables and graphs
Characterize data

e.g., Sample mean =
Statistics for Managers Using
Microsoft Excel, 4e © 2004
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∑X
i
n
Chap 1-7

8. 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
Drawing conclusions and/or making decisions
Statistics for Managers Using
concerning a population based on sample results.
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Chap 1-8
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9. Why We Need Data

To provide input to survey

To provide input to study

To measure performance of service or
production process

To evaluate conformance to standards

To assist in formulating alternative courses of
action
 To for Managers Using
Statistics satisfy curiosity
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Chap 1-9

10. Data Sources
Primary
Secondary
Data Collection
Data Compilation
Print or Electronic
Observation
Survey
Experimentation
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Chap 1-10

11. Reasons for Drawing a Sample

Less time consuming than a census

Less costly to administer than a census

Less cumbersome and more practical to
administer than a census of the targeted
population
Statistics for Managers Using
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Chap 1-11

12. Types of Samples Used

Non – probability Sample


Items included are chosen without regard to
their probability of occurrence
Probability Sample

Items in the sample are chosen on the basis
of known probabilities
Statistics for Managers Using
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Chap 1-12

13. Types of Samples Used
(continued)
Samples
Non-Probability
Samples
Judgement
Chunk
Quota Convenience
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Probability Samples
Simple
Random
Stratified
Systematic
Cluster
Chap 1-13

14. Probability Sampling

Items in the sample are chosen based on
known probabilities
Probability Samples
Simple
Systematic
Random
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Stratified
Cluster
Chap 1-14

15. Simple Random Samples

Every individual or item from the frame has an
equal chance of being selected

Selection may be with replacement or without
replacement

Samples obtained from table of random
numbers or computer random number
generators
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Chap 1-15

16. Systematic Samples

Decide on sample size: n

Divide frame of N individuals into groups of k
individuals: k=N/n

Randomly select one individual from the 1st
group

Select every kth individual thereafter
N = 64
n=8
First Group
Statistics for Managers Using
Microsoft Excel, k =© 2004
4e 8
Prentice-Hall, Inc.
Chap 1-16

17. Stratified Samples

Divide population into two or more subgroups (called
strata) according to some common characteristic

A simple random sample is selected from each subgroup,
with sample sizes proportional to strata sizes

Samples from subgroups are combined into one
Population
Divided
into 4
Statistics for
strata
Managers Using
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Sample
Chap 1-17

18. Cluster Samples

Population is divided into several “clusters,”
each representative of the population

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
Population
divided into
Statistics for Managers Using Randomly selected
16 clusters.
clusters for sample
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Chap 1-18
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19. Advantages and Disadvantages

Simple random sample and systematic sample



Stratified sample


Simple to use
May not be a good representation of the population’s
underlying characteristics
Ensures representation of individuals across the
entire population
Cluster sample
More cost effective
 Less efficient (need
Statistics for Managers Using larger sample to acquire the
same 4e © 2004
Microsoft Excel, level of precision)
Chap 1-19
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

20. Types of Data
Data
Categorical
Numerical
Examples:



Marital Status
Political Party
Eye Color
(Defined categories)
Discrete
Examples:

Number of Children
Defects per hour
(Counted items)
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
Continuous
Examples:


Weight
Voltage
(Measured characteristics)
Chap 1-20

21. Levels of Measurement
and Measurement Scales
Differences between
measurements, true
zero exists
Ratio Data
Differences between
measurements but no
true zero
Interval Data
Ordered Categories
(rankings, order, or
scaling)
Ordinal Data
Categories for
Statistics(no Managers
ordering or direction)
Using
Nominal Data
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Highest Level
Strongest forms of
measurement
Higher Level
Lowest Level
Weakest form of
measurement

22. Evaluating Survey Worthiness






What is the purpose of the survey?
Is the survey based on a probability sample?
Coverage error – appropriate frame?
Non-response error – follow up
Measurement error – good questions elicit good
responses
Sampling error – always exists
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Chap 1-22

23. Types of Survey Errors

Coverage error or selection bias


Non response error or bias


People who do not respond may be different from those
who do respond
Sampling error


Exists if some groups are excluded from the frame and
have no chance of being selected
Variation from sample to sample will always exist
Measurement error
Due to weaknesses
Statistics for Managers Using in question design, respondent
error, and
Microsoft Excel, 4e © interviewer’s effects on the respondent
2004
Chap 1-23
Prentice-Hall, Inc.


24. Types of Survey Errors
(continued)

Coverage error

Non response error

Sampling error

Measurement error
Statistics for Managers Using
Microsoft Excel, 4e © 2004
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Excluded from
frame
Follow up on
nonresponses
Random
differences from
sample to sample
Bad or leading
question
Chap 1-24

25. Chapter Summary

Reviewed why a manager needs to know statistics

Introduced key definitions:
♦ Population vs. Sample
♦ Primary vs. Secondary data types
♦ Qualitative vs. Qualitative data
♦ Time Series vs. Cross-Sectional data

Examined descriptive vs. inferential statistics

Described different types of samples
Reviewed data types and measurement levels
 Examined survey worthiness and types of survey
Statistics for Managers Using
errors
Microsoft Excel, 4e © 2004

Prentice-Hall, Inc.
Chap 1-25