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Chapter 8

Chapter 8. Confidence Intervals for One Population Mean. Section 8.1 Estimating a Population Mean. Point Estimate A point estimate of a parameter is the value of a statistic used to estimate the parameter. Definition 8.1. Confidence-Interval Estimate

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Chapter 8

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  1. Chapter 8 Confidence Intervals for One Population Mean

  2. Section 8.1 Estimating a Population Mean

  3. Point Estimate A point estimate of a parameter is the value of a statistic used to estimate the parameter. Definition 8.1

  4. Confidence-Interval Estimate Confidence interval (CI): An interval of numbers obtained from a point estimate of a parameter. Confidence level: The confidence we have that the parameter lies in the confidence interval (i.e., that the confidence interval contains the parameter). Confidence-interval estimate: The confidence level and confidence interval. Definition 8.2

  5. Twenty confidence intervals for the mean price of all new mobile homes, each based on a sample of 36 new mobile homes Figure 8.2

  6. Section 8.2 Confidence Intervals for One Population Mean when Sigma Is Known

  7. (a) 95.44% of all samples have means within 2 standard deviations of μ; (b) 100(1 − α)% of all samples have means within zα/2 standard deviations of μ Figure 8.3

  8. Procedure 8.1

  9. Ages, in years, of 50 randomly selected people in the civilian labor force Table 8.3

  10. 90% and 95% confidence intervals for μ, using the data in Table 8.3 Figure 8.5

  11. Section 8.3 Margin of Error

  12. Definition 8.3 & Figure 8.7 Figure 8.7 illustrates the margin of error.

  13. Formula 8.1

  14. Section 8.4 Confidence Intervals for One Population Mean When Sigma Is Unknown

  15. Standard normal curve and two t-curves Figure 8.8

  16. Basic Properties of t-Curves Property 1: The total area under a t-curve equals 1. Property 2: A t-curve extends indefinitely in both directions, approaching, but never touching, the horizontal axis as it does so. Property 3: A t-curve is symmetric about 0. Property 4: As the number of degrees of freedom becomes larger, t-curves look increasingly like the standard normal curve. Key Fact 8.6

  17. Values of Table 8.4

  18. Procedure 8.2

  19. Losses ($) for a sample of 25 pickpocket offenses Normal probability plot of the loss data in Table 8.5 Table 8.5 & Figure 8.10

  20. Normal probability plots for chicken consumption: (a) original data and (b) data with outlier removed Figure 8.11

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