figures for chapter 2
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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. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: FIGURES FOR CHAPTER 2](https://reader035.vdocuments.us/reader035/viewer/2022062422/5681426e550346895dae9429/html5/thumbnails/1.jpg)
©2005 Brooks/Cole - Thomson Learning
FIGURES FOR
CHAPTER 2
STATISTICAL INFERENCE
Click the mouse or use the arrow keys to move to the next page.Use the ESC key to exit this chapter.
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©2005 Brooks/Cole - Thomson Learning
Section 2.1 Example 1
![Page 3: FIGURES FOR CHAPTER 2](https://reader035.vdocuments.us/reader035/viewer/2022062422/5681426e550346895dae9429/html5/thumbnails/3.jpg)
©2005 Brooks/Cole - Thomson Learning
Section 2.1 Example 2
![Page 4: FIGURES FOR CHAPTER 2](https://reader035.vdocuments.us/reader035/viewer/2022062422/5681426e550346895dae9429/html5/thumbnails/4.jpg)
©2005 Brooks/Cole - Thomson Learning
Figure 2.1
The normal distribution: Y N(,2).
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©2005 Brooks/Cole - Thomson Learning
Section 2.2 Example 6
![Page 6: FIGURES FOR CHAPTER 2](https://reader035.vdocuments.us/reader035/viewer/2022062422/5681426e550346895dae9429/html5/thumbnails/6.jpg)
©2005 Brooks/Cole - Thomson Learning
Figure 2.2An unbiased estimator has a sampling distribution that is centered over the population parameter. Y is unbiased because its sampling distribution is centered over .
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©2005 Brooks/Cole - Thomson Learning
Figure 2.3The estimator is asymptotically unbiased; its sampling distribution becomes centered over 2 as n→∞.
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©2005 Brooks/Cole - Thomson Learning
Figure 2.4
The variance of Y decreases as the sample size increases.
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Figure 2.5
The comparative efficiency of three estimators.
![Page 10: FIGURES FOR CHAPTER 2](https://reader035.vdocuments.us/reader035/viewer/2022062422/5681426e550346895dae9429/html5/thumbnails/10.jpg)
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Figure 2.6
Simulated samplingdistributions (uniformpopulation).
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©2005 Brooks/Cole - Thomson Learning
Figure 2.7
Yi i.i.d.(,2).
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©2005 Brooks/Cole - Thomson Learning
Figure 2.8The least squares estimator is the value of that minimizes the sum of squares function S.
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©2005 Brooks/Cole - Thomson Learning
Figure 2.9
p-value for Example 10.
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Figure 2.10
Rejection regions.
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©2005 Brooks/Cole - Thomson Learning
Figure 2.12
Y is lognormally distributed: ln Y N(, 2).
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©2005 Brooks/Cole - Thomson Learning
Figure 2.13
Simulated samplingdistributions for the statistic t = √n(Y − )/sunder nonnormality.
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Figure 2.14A histogram of the monthly return on IBM stock, July 1963–June 1968.
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Figure 2.15Deterministic and stochastic trends.
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Figure 2.16The rate of return on IBM stock, July 1963–June 1968.