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Modul Statistik Bisnis II

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- 1. Statistics for Managers Using Microsoft® Excel 4th Edition Chapter 1 Introduction and Data Collection Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-1
- 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. Non-probability Samples Select a random sample using a random numbers table Identify types of data and levels of measurement Statistics for Managers Using Describe Microsoft Excel, the © 2004 types of survey error 4e different Chap 1-2 Prentice-Hall, Inc.
- 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 obtain reliable forecasts Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-3
- 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 Statistics for Managers Using the population Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-4
- 5. Population vs. Sample Population a b Sample cd b ef gh i jk l m n o p q rs t u v w x y z Measures used to describe the population are Using Statistics for Managerscalled parameters Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. c gi o n r u y Measures computed from sample data are called statistics Chap 1-5
- 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 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-6
- 7. Descriptive Statistics Collect data Present data e.g., Survey e.g., Tables and graphs Characterize data e.g., Sample mean = Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. ∑X i n Chap 1-7
- 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 Statistics for Managers Using concerning a population based on sample results. Microsoft Excel, 4e © 2004 Chap 1-8 Prentice-Hall, Inc.
- 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 for Managers Using Statistics satisfy curiosity Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-9
- 10. Data Sources Primary Secondary Data Collection Data Compilation Print or Electronic Observation Survey Experimentation Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-10
- 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 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-11
- 12. Types of Samples Used 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 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-12
- 13. Types of Samples Used (continued) Samples Non-Probability Samples Judgement Chunk Quota Convenience Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Probability Samples Simple Random Stratified Systematic Cluster Chap 1-13
- 14. Probability Sampling Items in the sample are chosen based on known probabilities Probability Samples Simple Systematic Random Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Stratified Cluster Chap 1-14
- 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 Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-15
- 16. Systematic Samples 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 N = 64 n=8 First Group Statistics for Managers Using Microsoft Excel, k =© 2004 4e 8 Prentice-Hall, Inc. Chap 1-16
- 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 Statistics for strata Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Sample Chap 1-17
- 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 Statistics for Managers Using Randomly selected 16 clusters. clusters for sample Microsoft Excel, 4e © 2004 Chap 1-18 Prentice-Hall, Inc.
- 19. Advantages and Disadvantages Simple random sample and systematic sample Stratified sample Simple to use May not be a good representation of the population’s underlying characteristics Ensures representation of individuals across the entire population Cluster sample More cost effective Less efficient (need Statistics for Managers Using larger sample to acquire the same 4e © 2004 Microsoft Excel, level of precision) Chap 1-19 Prentice-Hall, Inc.
- 20. Types of Data Data Categorical Numerical Examples: Marital Status Political Party Eye Color (Defined categories) Discrete Examples: Number of Children Defects per hour (Counted items) Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Continuous Examples: Weight Voltage (Measured characteristics) Chap 1-20
- 21. Levels of Measurement and Measurement Scales Differences between measurements, true zero exists Ratio Data Differences between measurements but no true zero Interval Data Ordered Categories (rankings, order, or scaling) Ordinal Data Categories for Statistics(no Managers ordering or direction) Using Nominal Data Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Highest Level Strongest forms of measurement Higher Level Lowest Level Weakest form of measurement
- 22. Evaluating Survey Worthiness What is the purpose of the survey? Is the survey based on a probability sample? Coverage error – appropriate frame? Non-response error – follow up Measurement error – good questions elicit good responses Sampling error – always exists Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-22
- 23. Types of Survey Errors Coverage error or selection bias Non response error or bias People who do not respond may be different from those who do respond Sampling error Exists if some groups are excluded from the frame and have no chance of being selected Variation from sample to sample will always exist Measurement error Due to weaknesses Statistics for Managers Using in question design, respondent error, and Microsoft Excel, 4e © interviewer’s effects on the respondent 2004 Chap 1-23 Prentice-Hall, Inc.
- 24. Types of Survey Errors (continued) Coverage error Non response error Sampling error Measurement error Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Excluded from frame Follow up on nonresponses Random differences from sample to sample Bad or leading question Chap 1-24
- 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 Statistics for Managers Using errors Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 1-25

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