Chapter 1 – A First Look at Statistics and Data Collection

1. KVANLI PAVUR KEELING Concise Managerial Statistics Chapter 1 –A First Look atStatistics andData Collection Slides prepared by Jeff Heyl Lincoln University ©2006 Thomson/South-Western

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 (all votes cast) Sample (selected votes for observation) Population Verses a Sample 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. Level of Measurement Property Nominal Ordinal Interval Ratio Order of data is meaningful N Y Y Y Difference between data values is meaningful N N Y Y Zero point represents total absence N N N Y Data Levels and Measurement Table 1.1

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

10. Numerical data EXAMPLES OF DISCRETE DATA 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 Data Types Qualitative Quantitative Levels of Measurement Nominal Ordinal Ratio Interval EXAMPLES OF CONTINUOUS DATA Discrete 1. Interval: Actual temperature, º F 2. Ratio: Weight of packaged dog food Discrete or continuous Types of Data Figure 1.2

11. 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

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

13. Advantages and Disadvantages of Nonrandom Sampling • Advantages • 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

14. Generating Random Numbers Figure 1.3 a, b, c