Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-1

Statistics for ManagersUsing Microsoft® Excel

4th Edition

Chapter 1

Introduction and Data Collection

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 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 Describe the different types of survey error

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-3

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 Prentice-Hall, Inc. Chap 1-4

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 the population

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-5

Population vs. Sample

a b c d

ef gh i jk l m n

o p q rs t u v w

x y z

Population Sample

b c

g i n

o r u

y

Measures used to describe the population are called parameters

Measures computed from sample data are called statistics

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-6

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 Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-7

Descriptive Statistics

Collect data e.g., Survey

Present data e.g., Tables and graphs

Characterize data e.g., Sample mean =

iX

n

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-8

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 concerning a population based on sample results.

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-9

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 satisfy curiosity

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-10

Data Sources

SecondaryData Compilation

Observation

Experimentation

Print or Electronic

Survey

PrimaryData Collection

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-11

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 Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-12

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

Types of Samples Used

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-13

Types of Samples Used

Quota

Samples

Non-Probability Samples

Judgement Chunk

Probability Samples

Simple Random

Systematic

Stratified

ClusterConvenience

(continued)

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-14

Probability Sampling

Items in the sample are chosen based on known probabilities

Probability Samples

Simple Random

Systematic Stratified Cluster

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-15

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

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-16

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

Systematic Samples

N = 64

n = 8

k = 8

First Group

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-17

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

strata

Sample

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-18

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 16 clusters. Randomly selected

clusters for sample

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-19

Advantages and Disadvantages

Simple random sample and systematic sample Simple to use May not be a good representation of the population’s

underlying characteristics Stratified sample

Ensures representation of individuals across the entire population

Cluster sample More cost effective Less efficient (need larger sample to acquire the

same level of precision)

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-20

Types of Data

Data

Categorical Numerical

Discrete Continuous

Examples:

Marital Status Political Party Eye Color (Defined categories) Examples:

Number of Children Defects per hour (Counted items)

Examples:

Weight Voltage (Measured characteristics)

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc.

Levels of Measurementand Measurement Scales

Interval Data

Ordinal Data

Nominal Data

Highest Level

Strongest forms of measurement

Higher Level

Lowest Level

Weakest form of measurement

Categories (no ordering or direction)

Ordered Categories (rankings, order, or scaling)

Differences between measurements but no true zero

Ratio DataDifferences between measurements, true zero exists

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-22

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

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-23

Types of Survey Errors

Coverage error or selection bias Exists if some groups are excluded from the frame and

have no chance of being selected

Non response error or bias People who do not respond may be different from those

who do respond

Sampling error Variation from sample to sample will always exist

Measurement error Due to weaknesses in question design, respondent

error, and interviewer’s effects on the respondent

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-24

Types of Survey Errors

Coverage error

Non response error

Sampling error

Measurement error

Excluded from frame

Follow up on nonresponses

Random differences from sample to sample

Bad or leading question

(continued)

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-25

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

errors