1. Chapter 1 – A First Look at Statistics and Data Collection
Introduction to Business Statistics, 6e
Kvanli, Pavur, Keeling
Chapter 1 – A First Look at Statistics and Data Collection
Slides prepared by Jeff Heyl, Lincoln University
©2003 South-Western/Thomson Learning™

2. Areas of Business that Rely on Statistics
Quality Improvement
Product Planning
Forecasting
Yearly Reports
Personnel Management
Market Research

3. Basic Definitions
Descriptive Statistics: the collection and description of data
Inferential Statistics: analyzing, decision making or estimation based on the data
Population: the set of all possible measurements that is of interest
Sample: the portion of the population from which information is gathered

4. Population Verses a Sample
Population (all votes cast)
Sample (selected votes for observation)
Figure 1.1

5. Basic Definitions
Simple Random Sample: a sample in which each item in the population has an equal chance of being selected
Census: the selection of all population items
Parameter: a measure calculated from the population
Statistic: a measure calculated from the sample

6. Basic Definitions
Discrete Data: data that contains only integers or counting numbers – usually the result of counting something
Continuous Data: any value over a particular range is possible – usually the result of measuring something

7. Level of Measurement for Numerical Data
Nominal data are merely labels or assigned numbers
Ordinal data can be arranged in order such as worst to best or best to worst
Interval data can be arranged in order and the difference between numbers has meaning
Ratio data differ from interval data in that there is a definite zero point

8. Data Levels and MeasurementY N
Zero point represents total absence
Difference between data values is meaningful
Order of data is meaningful
Ratio
Interval
Ordinal
Nominal
Property
Level of Measurement
Table 1.1

9. Types of Data Data Types Levels of Measurement Numerical data
Qualitative
Quantitative
Data Types
Nominal
Ordinal
Interval
Ratio
Levels of Measurement
Discrete
Discrete or continuous
Figure 1.2

10. Types of Data EXAMPLES OF DISCRETE DATA
Numerical data
Qualitative
Nominal
Ordinal
Discrete
Quantitative
Interval
Ratio
Discrete or continuous
Data Types
Levels of Measurement
1. Nominal: Ownership status of resident dweller
(1 = own, 2 = rent)
2. Ordinal: Level of customer satisfaction
(1 = very dissatisfied, 2 = somewhat dissatisfied, 3 = somewhat satisfied, 4 = very satisfied)
3. Interval: Person’s score on IQ test
4. Ratio: Number of defective lightbulbs in a carton
1. Interval: Actual temperature, º F
2. Ratio: Weight of packaged dog food
EXAMPLES OF DISCRETE DATA
EXAMPLES OF CONTINUOUS DATA
Figure 1.2

11. Data Sources
Primary data come from an original (primary) source and are collected with a specific research question in mind, often using a designed procedure
Secondary data represent previously recorded data collected for another purpose or as part of a regularly scheduled data collection procedure

12. Random Sampling versus Nonrandom Sampling
Random Sampling ensures that the sample obtain is representative of the population
Nonrandom Samples or nonprobability samples are generated using a deliberate selection procedure
Convenience sampling
Judgement sampling
Quota sampling

13. Advantages and Disadvantages of Random Sampling
Can generalize beyond the sample
Disadvantages:
Data may be difficult to obtain
Data may be expensive to collect

14. Advantages and Disadvantages of Nonrandom Sampling
Data are more easily obtained
May provide enough information to make a decision
Data can be used as an informal base of knowledge in preparation for a later random sample
The primary disadvantage is that the information can not be generalized beyond the sample

15. Generating Random Numbers
Figure 1.3

16. Generating Random Numbers
Figure 1.4