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KVANLI PAVUR KEELING. Concise Managerial Statistics. Chapter 1 – A First Look at Statistics and Data Collection. Slides prepared by Jeff Heyl Lincoln University. ©2006 Thomson/South-Western. Areas of Business that Rely on Statistics. Quality Improvement. Product Planning Forecasting

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Chapter 1 – A First Look at Statistics and Data Collection


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slide1

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

areas of business that rely on statistics
Areas of Business that Rely on Statistics
  • Quality Improvement
  • Product Planning
  • Forecasting
  • Yearly Reports
  • Personnel Management
  • Market Research
basic definitions
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
population verses a sample

Population (all votes cast)

Sample (selected votes for observation)

Population Verses a Sample

Figure 1.1

basic definitions5
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
basic definitions6
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
level of measurement for numerical data
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
data levels and measurement

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

types of data

Numerical data

Qualitative

Quantitative

Data Types

Levels of Measurement

Nominal

Ordinal

Ratio

Interval

Discrete

Discrete or continuous

Types of Data

Figure 1.2

types of data10

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

random sampling versus nonrandom sampling
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
advantages and disadvantages of random sampling
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
advantages and disadvantages of nonrandom sampling
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
generating random numbers
Generating Random Numbers

Figure 1.3 a, b, c