1. Chapter 1 The Where, Why, and How of Data Collection
Business Statistics:
A Decision-Making Approach
8th Edition
Chapter 1 The Where, Why, and How of Data Collection
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

2. Chapter Goals After completing this chapter, you should be able to:
Describe key data collection methods
Know key definitions:
Population vs. Sample Primary vs. Secondary data types
Qualitative vs. Qualitative data Time Series vs. Cross-Sectional data
Explain the difference between descriptive and
inferential procedures
Describe different sampling methods
Construct and interpret graphs
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

3. Procedures of Statistics
Descriptive procedures
Collecting, presenting, and describing data
Inferential procedures
Drawing conclusions and/or making decisions concerning a population based only on sample data
Goal: Convert data into meaningful information!
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

4. Descriptive Procedures
Collect data
e.g., Survey, Observation,
Experiments
Present data
e.g., Charts and graphs
Describe data
e.g., Sample mean =
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

5. Inferential Procedures
Making statements about a population by examining sample results
Sample statistics Population parameters
(known) Inference
unknown, but can
be estimated from
sample evidence
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

6. Techniques for Inferential Procedures
Drawing conclusions and/or making decisions concerning a population based on sample results.
Estimation
e.g., Estimate the population mean weight using the sample mean weight
Hypothesis Testing
e.g., Use sample evidence to test the claim that the population mean weight is 120 pounds
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

7. Procedures for Collecting Data (MKTG 300 course and Video Clip #14: Samples and Surveys)
Data Collection Procedures
Experiments
Written questionnaires
Telephone surveys
Direct observation and personal interview
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

8. Population vs. Sample Population Sample a b c d b c ef gh i jk l m n
o p q rs t u v w
x y z
b c
g i n
o r u y
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

9. Populations and Samples
A population is the entire collection of things under consideration and referred to as the frame
The sampling unit is each object or individual in the frame
A parameter is a summary measure computed to describe a characteristic of the population
A sample is a subset of the population selected for analysis
A statistic is a summary measure computed to describe a characteristic of the sample drawn from the population
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

10. Why Sample? Less time consuming than a census
Less costly to administer than a census
It is possible to obtain statistical results of a sufficiently high precision based on samples
Strive for representative samples to reflect the population of interest accurately!
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

11. Nonstatistical Sampling
Sampling Techniques
Sampling Techniques
Nonstatistical Sampling
Statistical Sampling
Simple
Random
Convenience
Systematic
Judgment
Cluster
Stratified
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

12. Nonstatistical Sampling
Convenience
Collected in the most convenient manner for the researcher
Judgment
Based on judgments about who in the population would be most likely to provide the needed information
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

13. (Probability Sampling)
Statistical Sampling
Items of the sample are chosen based on known or calculable probabilities
Statistical Sampling
(Probability Sampling)
Simple Random
Stratified
Systematic
Cluster
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

14. Simple Random Sampling
Every possible sample of a given size has an equal chance of being selected
Selection may be with replacement or without replacement
The sample can be obtained using a table of random numbers or computer random number generator
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

15. Stratified Random Sampling (Please Watch Video Clip #14: Samples and Surveys) 
Divide population into subgroups (called strata) according to some common characteristic
e.g., gender, income level
Select a simple random sample from each subgroup
Combine samples from subgroups into one
Population
Divided
into 4
strata
Sample
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

16. Systematic Random Sampling
Decide on sample size: n
Divide ordered (e.g., alphabetical) 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
k = 8
First Group
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

17. Cluster Sampling
Divide population into several “clusters,” each representative of the population (e.g., county)
Select a simple random sample of clusters
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
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

18. Data Types (Please see the Video Clip Introduction to Variables)
Examples:
Marital Status
Political Party
Eye Color
(Defined categories)
Examples:
Number of Children
Defects per hour
(Counted items)
Examples:
Weight
Voltage
(Measured
characteristics)
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

19. Data Types Time Series Data Cross Sectional Data
Ordered data values observed over time
Cross Sectional Data
Data values observed at a single point in time
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

20. Data Types Sales (in $1000’s) 2003 2004 2005 2006 Atlanta 435 460 475
490
Boston
320
345
375
395
Cleveland
405
390
410
Denver
260
270
285
280
Time Series Data
Cross Sectional
Data
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

21. Data Measurement Levels (Please see the Video Clip: Scales of Measurement)
Highest Level
Complete Analysis
Measurements
e.g., temperature
Ratio/Interval Data
Rankings
Ordered Categories
e.g., age range 25-34
Higher Level
Mid-level Analysis
Ordinal Data
Lowest Level
Basic Analysis
Categorical Codes e.g., ID Numbers, gender
Nominal Data
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

22. Steps to Categorizing Data
Identify each factor in the data set
Determine if the data are time-series or cross-sectional
Determine which factors are quantitative and which are qualitative
Determine the level of data measurement
e.g., nominal, ordinal
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

23. Chapter Summary Reviewed key data collection methods
Introduced key definitions:
Population vs. Sample Primary vs. Secondary data types
Qualitative vs. Quantitative data Time Series vs. Cross-Sectional data
Examined descriptive vs. inferential procedures
Described different sampling techniques
Reviewed data types and measurement levels
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall

24. Printed in the United States of America.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.
Printed in the United States of America.
Copyright ©2011 Pearson Education, Inc. publishing as Prentice Hall