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FIGURES FOR

CHAPTER 2

STATISTICAL INFERENCE

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Section 2.1 Example 1

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Section 2.1 Example 2

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Figure 2.1

The normal distribution: Y N(,2).

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Section 2.2 Example 6

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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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Figure 2.3The estimator is asymptotically unbiased; its sampling distribution becomes centered over 2 as n→∞.

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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.

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Figure 2.6

Simulated samplingdistributions (uniformpopulation).

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Figure 2.7

Yi i.i.d.(,2).

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

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Figure 2.9

p-value for Example 10.

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Figure 2.10

Rejection regions.

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Figure 2.12

Y is lognormally distributed: ln Y N(, 2).

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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.