1 / 19

FIGURES FOR CHAPTER 2

STATISTICAL INFERENCE. FIGURES FOR CHAPTER 2. Click the mouse or use the arrow keys to move to the next page. Use the ESC key to exit this chapter. Section 2.1 Example 1. Section 2.1 Example 2. Figure 2.1 The normal distribution: Y ~ N ( m , s 2 ). Section 2.2 Example 6.

nelly
Download Presentation

FIGURES FOR CHAPTER 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. STATISTICAL INFERENCE FIGURES FORCHAPTER 2 Click the mouse or use the arrow keys to move to the next page. Use the ESC key to exit this chapter.

  2. Section 2.1 Example 1

  3. Section 2.1 Example 2

  4. Figure 2.1The normal distribution: Y~N(m ,s2).

  5. Section 2.2 Example 6

  6. Figure 2.2An unbiased estimator has a sampling distribution that is centered over the population parameter. Yis unbiased because its sampling distribution is centered over m.

  7. Figure 2.3The estimator is asymptotically unbiased; its sampling distribution becomes centered over s2 as n→∞.

  8. Figure 2.4The variance of Ydecreases as the sample size increases.

  9. Figure 2.5The comparative efficiency of three estimators.

  10. Figure 2.6Simulated samplingdistributions (uniformpopulation).

  11. Figure 2.7Yi~ i.i.d.(m,s2).

  12. Figure 2.8The least squares estimator is the value of m that minimizes the sum of squares function S.

  13. Figure 2.9p-value for Example 10.

  14. Figure 2.10Rejection regions.

  15. Figure 2.12Yis lognormally distributed: ln Y~N(m , s2).

  16. Figure 2.13Simulated samplingdistributions for the statistict = √n(Y − m)/sunder nonnormality.

  17. Figure 2.14A histogram of the monthly return on IBM stock, July 1963–June 1968.

  18. Figure 2.15Deterministic and stochastic trends.

  19. Figure 2.16The rate of return on IBM stock, July 1963–June 1968.

More Related