Introduction and Data Collection
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The document discusses key concepts in statistics for managers, including defining populations and samples, descriptive versus inferential statistics, different sampling methods, types of data and measurement scales, and sources of error in surveys. It aims to explain why understanding statistics is important for managers to properly analyze and present information, make decisions based on data, and improve processes. The chapter goals are to introduce fundamental statistical concepts and methods in data collection.
Introduction and Data Collection
- 1. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-1 Statistics for Managers Bahan Kuliah Pra MM 3 Oktober 2010 Kuliah Pertama Introduction and Data Collection Dr. Syuhada Sufian, MSIE
- 2. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-2 Chapter Goals After completing this chapter, you should be able to: Explain key definitions: ♦ Population vs. Sample ♦ Primary vs. Secondary Data ♦ Parameter vs. Statistic ♦ Descriptive vs. Inferential Statistics Describe key data collection methods Describe different sampling methods Probability Samples vs. Nonprobability Samples Select a random sample using a random numbers table Identify types of data and levels of measurement Describe the different types of survey error
- 3. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-3 Why a Manager Needs to Know about Statistics To know how to: properly present information draw conclusions about populations based on sample information improve processes decession obtain reliable forecasts
- 4. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-4 Key Definitions A population (universe) is the collection of all items or things under consideration A sample is a portion of the population selected for analysis A parameter is a summary measure that describes a characteristic of the population A statistic is a summary measure computed from a sample to describe a characteristic of the population
- 5. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-5 Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z Population Sample b c g i n o r u y Measures used to describe the population are called parameters Measures computed from sample data are called statistics
- 6. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-6 Two Branches of Statistics Descriptive statistics Collecting, summarizing, and describing data Inferential statistics Drawing conclusions and/or making decisions concerning a population based only on sample data
- 7. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-7 Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Characterize data e.g., Sample mean = iX n ∑
- 8. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-8 Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.
- 9. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-9 Why We Need Data To provide input to survey To provide input to study To measure performance of service or production process To evaluate conformance to standards To assist in formulating alternative courses of action To satisfy curiosity (keunikan)
- 10. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-10 Data Sources Secondary Data Compilation Observation Experimentation Print or Electronic Survey Primary Data Collection
- 11. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-11 Reasons for Drawing a Sample Less time consuming than a census Less costly to administer than a census Less cumbersome and more practical to administer than a census of the targeted population
- 12. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-12 Non – probability Sample Items included are chosen without regard to their probability of occurrence Probability Sample Items in the sample are chosen on the basis of known probabilities Types of Samples Used
- 13. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-13 Types of Samples Used Quota Samples Non-Probability Samples Judgement Chunk Probability Samples Simple Random Systematic Stratified Cluster Convenience (continued)
- 14. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-14 Probability Sampling Items in the sample are chosen based on known probabilities Probability Samples Simple Random Systematic Stratified Cluster
- 15. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-15 Simple Random Samples Every individual or item from the frame has an equal chance of being selected Selection may be with replacement or without replacement Samples obtained from table of random numbers or computer random number generators
- 16. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-16 Decide on sample size: n Divide 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 Systematic Samples N = 64 n = 8 k = 8 First Group
- 17. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-17 Stratified Samples Divide population into two or more subgroups (called strata) according to some common characteristic A simple random sample is selected from each subgroup, with sample sizes proportional to strata sizes Samples from subgroups are combined into one Population Divided into 4 strata Sample
- 18. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-18 Cluster Samples Population is divided into several “clusters,” each representative of the population A simple random sample of clusters is selected 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
- 19. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-19 Advantages and Disadvantages Simple random sample and systematic sample Simple to use May not be a good representation of the population’s underlying characteristics Stratified sample Ensures representation of individuals across the entire population Cluster sample More cost effective Less efficient (need larger sample to acquire the same level of precision)
- 20. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-20 Types of Data Data Categorical Numerical Discrete Continuous Examples: Marital Status Political Party Eye Color (Defined categories) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics)
- 21. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Levels of Measurement and Measurement Scales Interval Data Ordinal Data Nominal Data Highest Level Strongest forms of measurement Higher Level Lowest Level Weakest form of measurement Categories (no ordering or direction) Ordered Categories (rankings, order, or scaling) Differences between measurements but no true zero Ratio Data Differences between measurements, true zero exists
- 22. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-22 Evaluating Survey Worthiness What is the purpose of the survey? Is the survey based on a probability sample? Coverage error – appropriate frame? Nonresponse error – follow up Measurement error – good questions elicit good responses Sampling error – always exists
- 23. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-23 Types of Survey Errors Coverage error or selection bias Exists if some groups are excluded from the frame and have no chance of being selected Non response error or bias People who do not respond may be different from those who do respond Sampling error Variation from sample to sample will always exist Measurement error Due to weaknesses in question design, respondent error, and interviewer’s effects on the respondent
- 24. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-24 Types of Survey Errors Coverage error Non response error Sampling error Measurement error Excluded from frame Follow up on nonresponses Random differences from sample to sample Bad or leading question (continued)
- 25. Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-25 Chapter Summary Reviewed why a manager needs to know statistics Introduced key definitions: ♦ Population vs. Sample ♦ Primary vs. Secondary data types ♦ Qualitative vs. Qualitative data ♦ Time Series vs. Cross-Sectional data Examined descriptive vs. inferential statistics Described different types of samples Reviewed data types and measurement levels Examined survey worthiness and types of survey errors