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Transcript

  • 1. Chapter 1 Why Study Statistics? Statistics for Business and Economics 6 th Edition
  • 2. Chapter Goals
    • After completing this chapter, you should be able to:
    • Explain how decisions are often based on incomplete information
    • Explain key definitions:
      •  Population vs. Sample
      •  Parameter vs. Statistic
      •  Descriptive vs. Inferential Statistics
    • Describe random sampling
    • Explain the difference between Descriptive and Inferential statistics
  • 3. Dealing with Uncertainty
    • Everyday decisions are based on incomplete information
    • Consider:
    • The price of IBM stock will be higher in six months than it is now.
    • If the federal budget deficit is as high as predicted, interest rates will remain high for the rest of the year.
  • 4. Dealing with Uncertainty
    • Because of uncertainty, the statements should be modified:
    • The price of IBM stock is likely to be higher in six months than it is now.
    • If the federal budget deficit is as high as predicted, it is probable that interest rates will remain high for the rest of the year.
    (continued)
  • 5. Key Definitions
    • A population is the collection of all items of interest or under investigation
        • N represents the population size
    • A sample is an observed subset of the population
        • n represents the sample size
    • A parameter is a specific characteristic of a population
    • A statistic is a specific characteristic of a sample
  • 6. 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 Values calculated using population data are called parameters Values computed from sample data are called statistics
  • 7. Examples of Populations
    • Names of all registered voters in the United States
    • Incomes of all families living in Daytona Beach
    • Annual returns of all stocks traded on the New York Stock Exchange
    • Grade point averages of all the students in your university
  • 8. Random Sampling
    • Simple random sampling is a procedure in which
    • each member of the population is chosen strictly by chance,
    • each member of the population is equally likely to be chosen,
    • and
    • every possible sample of n objects is equally likely to be chosen
    • The resulting sample is called a random sample
  • 9. Descriptive and Inferential Statistics
    • Two branches of statistics:
    • Descriptive statistics
      • Collecting, summarizing, and processing data to transform data into information
    • Inferential statistics
      • provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge
  • 10. Descriptive Statistics
    • Collect data
      • e.g., Survey
    • Present data
      • e.g., Tables and graphs
    • Summarize data
      • e.g., Sample mean =
  • 11. 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
    Inference is the process of drawing conclusions or making decisions about a population based on sample results
  • 12. The Decision Making Process Begin Here: Identify the Problem Data Information Knowledge Decision Descriptive Statistics, Probability, Computers Experience, Theory, Literature, Inferential Statistics, Computers
  • 13. Chapter Summary
    • Reviewed incomplete information in decision making
    • Introduced key definitions:
      • Population vs. Sample
      • Parameter vs. Statistic
      • Descriptive vs. Inferential statistics
    • Described random sampling
    • Examined the decision making process

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