Statistics For Managers Using Microsoft Excel: 4 Edition

Statistics for Managers Using Microsoft® Excel 4th Edition Chapter 1 Introduction and Data Collection

Statistics for Managers Using Microsoft Excel, 4e © 2004 PrenticeHall, Inc.

Chap 1-1

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. Nonprobability 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 1-2

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 1-3

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 1-4

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

c

gi o

n r

u

y Measures computed from sample data are called statistics Chap 1-5

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 1-6

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 Prentice-Hall, Inc.

X

i

n

Chap 1-7

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 1-8

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 1-9

Data Sources Primary

Secondary

Data Collection

Data Compilation Print or Electronic

Observation

Survey

Experimentation

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

Chap 1-10

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 1-11

Types of Samples Used 

Nonprobability 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 Microsoft Excel, 4e © 2004 Prentice-Hall, Inc.

Chap 1-12

Types of Samples Used (continued)

Samples

Non-Probability Samples

Judgement Quota

Chunk Convenience

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

Probability Samples

Simple Random

Stratified

Systematic

Cluster

Chap 1-13

Probability Sampling 

Items in the sample are chosen based on known probabilities Probability Samples

Simple Random

Systematic

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

Stratified

Cluster

Chap 1-14

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 1-15

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

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

Chap 1-16

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 1-17

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 1-18

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 larger sample to acquire the same level of precision)

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

Chap 1-19

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)

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

Continuous Examples:  

Weight Voltage (Measured characteristics) Chap 1-20

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 (no ordering or direction)

Nominal Data

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

Highest Level Strongest forms of measurement

Higher Level

Lowest Level Weakest form of measurement

Evaluating Survey Worthiness     



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

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

Chap 1-22

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 in question design, respondent error, and interviewer’s effects on the respondent

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

Chap 1-23

Types of Survey Errors (continued) 

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

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

Chap 1-24

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



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

Chap 1-25