Probability statistics for engineers and scientists 9th by walpole myers chapter 09

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Probability  statistics for engineers and scientists 9th by walpole myers chapter 09

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Chapter One- and Two-Sample Estimation Problems Copyright © 2010 Pearson Addison-Wesley All rights reserved Section 9.1 Introduction Copyright © 2010 Pearson Addison-Wesley All rights reserved Section 9.2 Statistical Inference Copyright © 2010 Pearson Addison-Wesley All rights reserved Section 9.3 Classical Methods of Estimation Copyright © 2010 Pearson Addison-Wesley All rights reserved Definition 9.1 Copyright © 2010 Pearson AddisonWesley All rights reserved 9-5 Definition 9.2 Copyright © 2010 Pearson AddisonWesley All rights reserved 9-6 Figure 9.1 Sampling distributions of different estimators of  Copyright © 2010 Pearson AddisonWesley All rights reserved 9-7 Section 9.4 Single Sample: Estimating the Mean Copyright © 2010 Pearson Addison-Wesley All rights reserved Figure 9.2 P(-z/2 < Z < z/2) = 1- Copyright © 2010 Pearson AddisonWesley All rights reserved 9-9 Figure 9.3 Interval estimates of  for different samples Copyright © 2010 Pearson AddisonWesley All rights reserved - 10 Section 9.9 Paired Observations Copyright © 2010 Pearson Addison-Wesley All rights reserved Table 9.1 Data for Example 9.13 Copyright © 2010 Pearson AddisonWesley All rights reserved - 20 Section 9.10 Single Sample: Estimating a Proportion Copyright © 2010 Pearson Addison-Wesley All rights reserved Figure 9.6 Error in estimating p by pˆ Copyright © 2010 Pearson AddisonWesley All rights reserved - 22 Theorem 9.3 Copyright © 2010 Pearson AddisonWesley All rights reserved - 23 Theorem 9.4 Copyright © 2010 Pearson AddisonWesley All rights reserved - 24 Theorem 9.5 Copyright © 2010 Pearson AddisonWesley All rights reserved - 25 Section 9.11 Two Samples: Estimating the Difference between Two Proportions Copyright © 2010 Pearson Addison-Wesley All rights reserved Section 9.12 Single Sample: Estimating the Variance Copyright © 2010 Pearson Addison-Wesley All rights reserved Figure 9.7 P ( x1 2  X x 2 ) = 1 Copyright © 2010 Pearson AddisonWesley All rights reserved - 28 Section 9.13 Two Samples: Estimating the Ratio of Two Variances Copyright © 2010 Pearson Addison-Wesley All rights reserved Figure 9.8 P [f1  (v1 ,v ) F f 2 (v1 , v )] =   Copyright © 2010 Pearson AddisonWesley All rights reserved - 30 Section 9.14 Maximum Likelihood Estimation (Optional) Copyright © 2010 Pearson Addison-Wesley All rights reserved Definition 9.3 Copyright © 2010 Pearson AddisonWesley All rights reserved - 32 Section 9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters Copyright © 2010 Pearson Addison-Wesley All rights reserved ... 9-9 Figure 9.3 Interval estimates of  for different samples Copyright © 2010 Pearson AddisonWesley All rights reserved - 10 Figure 9.4 Error in estimating _ by x Copyright © 2010 Pearson AddisonWesley... T < t/2) = 1 Copyright © 2010 Pearson AddisonWesley All rights reserved - 14 Section 9.5 Standard Error of a Point Estimate Copyright © 2010 Pearson Addison-Wesley All rights reserved Section... Paired Observations Copyright © 2010 Pearson Addison-Wesley All rights reserved Table 9.1 Data for Example 9.13 Copyright © 2010 Pearson AddisonWesley All rights reserved - 20 Section 9.10 Single

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Mục lục

  • Slide 1

  • Slide 2

  • Slide 3

  • Slide 4

  • Definition 9.1

  • Definition 9.2

  • Figure 9.1 Sampling distributions of different estimators of q

  • Slide 8

  • Figure 9.2 P(-za/2 < Z < za/2) = 1-a

  • Figure 9.3 Interval estimates of m for different samples

  • Figure 9.4 Error in estimating m by x

  • Theorem 9.1

  • Theorem 9.2

  • Figure 9.5 P(-ta /2 < T < ta /2) = 1-a

  • Slide 15

  • Slide 16

  • Slide 17

  • Slide 18

  • Slide 19

  • Table 9.1 Data for Example 9.13

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