1. 1
Chapter

2. 1 An Introduction to Business Statistics Chapter CHAPTER 1 MAP
1.1 Business Statistics and Their Uses
1.2 Data
1.3 Descriptive and Inferential Statistics
1.4 Ethics and Statistics  It’s a Dangerous World of Data Out There

3. 1.1 Business Statistics and Their Uses
the mathematical science that deals with the collection, analysis, and presentation of data, which can then be used as a basis for inference and induction

4. Business Statistics and Their Uses
Examples of how business use statistics:
Marketing Research
Focus group data, customer surveys
Advertising
Household surveys, TV viewing habits
Operations
Quality control, reliability
Finance and Economics
Data on income, credit risk, unemployment

5. 1.2 Data
Data
values assigned to observations or measurements
Information
data that are transformed into useful facts that can be used for a specific purpose, such as making a decision

6. Data Data: Raw facts or measurements of interest
Table 1.1 | Golf-Score Data
Date Score 6/ 6/ 6/ 7/ 7/ 7/
Each individual value is considered a data point

7. Information
Analyzing the data can provide information for decision making
Table 1.1 | Golf-Score Data
Date Score 6/ 6/ 6/ 7/ 7/ 7/
Did a new driver after 7/1 change the average golf score?

8. The Sources of Data Primary data
data that you have collected for your own use
Secondary data
data collected by someone else

9. The Sources of Data Primary data Secondary data Advantages:
collected by the person or organization who uses the data
Disadvantages:
Can be expensive and time-consuming to gather
Advantages:
Readily available
Less expensive to collect
Disadvantages:
No control over how the data was collected
Less reliable unless collected and recorded accurately

10. Primary data collection methods
Direct Observation or Focus Group
Experiments
Surveys or Questionnaires
Observing subjects in their natural environment
Example: Watching to see if drivers stop at a stop sign
Treatments are applied in controlled conditions
Example: Crop growth from different plots using different fertilizers
Subjects are asked to respond to questions or discuss attitudes
Example: surveys to customers to assess service quality

11. Figure 1.2 | An Example of a Survey
To encourage respondents to participate, an effective survey will state its purpose in the beginning
Personal
demographic questions are often last, when respondents feel more comfortable with the process

12. Bias
The manner in which survey questions are asked can affect responses
Bias can occur when a question is stated in a way that encourages or leads a respondent to a particular answer
Example: “Do you agree that the current overly complex tax code should be simplified and made more fair?”

13. The Two Main Types of Data
Qualitative
Data
Quantitative
Data
Classified by descriptive terms
Counted
Measured
Examples:
Marital Status
Political Party
Eye Color
(Defined categories)
Described by numerical values
Examples:
Number of Children
Defects per hour
(Counted items)
Examples:
Weight
Voltage
(Measured characteristics)

14. Classifying Data by Level of Measurement
Figure 1.4 |
Two Main Types of Data and their
Corresponding Levels
Types of Data
Qualitative
Quantitative
Nominal
Ordinal
Interval
Ratio

15. Classifying Data by Level of Measurement

16. Time Series vs. Cross-Sectional Data
Time Series Data
values that correspond to specific measurements taken over a range of time periods
Cross Section Data
values collected from a number of subjects during a single time period

17. Time Series vs. Cross-Sectional Data
Table 1.3 | Unemployment Rate Data, 2008–2012
Unemployment Rate
Year
USA %
CA %
DE %
MI %
TX %
2008
4.9
5.9
3.8
7.1
4.4
2009
7.6
10.1
6.7
11.6
6.4
2010
9.7
12.3
8.8
13.7
8.2
2011
9.0
12.4
8.5
10.7
8.3
2012
10.9
7.0
7.3
Cross- Sectional Data
Time Series Data

18. Time Series vs. Cross-Sectional Data
Figure A Time Series Graph of U.S. Unemployment Rates, 2008–2012
Figure A Cross-Sectional Graph of 2012 Unemployment Rates

19. 1.3 Descriptive and Inferential Statistics
Descriptive statistics
Collecting, summarizing, and displaying data
Inferential statistics
making claims or conclusions about the data based on a sample

20. Population vs. Sample Population Sample
represents all possible subjects that are of interest in a particular study
Sample
refers to a portion of the
population that is
representative of the
population from which
it was selected

21. Parameter vs. Statistic
Parameter – a described characteristic about a population
Statistic – a described characteristic about a sample
Population
Sample
Values calculated using population data are called parameters
Values computed from sample data are called statistics

22. Inferential Statistics
Making statements about a population by examining sample results
Example:
Observed sample statistic (known)
Estimated population parameter (unknown, but can be estimated from sample evidence)
Inference

23. Inferential Statistics
Figure Using Inferential Statistics for Quality Control Purposes: An Example

24. 1.4 Ethics and Statistics – It’s a Dangerous World of Data Out There
Biased sample – a sample that does not represent the intended population
can lead to distorted findings
biased sampling can occur intentionally or unintentionally
results can be manipulated by how we ask questions and who is responding to them

25. Ways to Misuse Statistics
Changing the graph scale
Should avoid distortion that might convey the wrong message:
Choosing a sample that is not representative of the population
Avoid bias by randomly sampling from the population
vs.

26. 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.