Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 6 Some Continuous Probability...

Chapter 6

Some Continuous Probability Distributions

Section 6.1

Continuous Uniform Distribution

Figure 6.1 The density function for a random variable on the interval 1,3

Theorem 6.1

Section 6.2

Normal Distribution

Figure 6.2 The normal curve

Figure 6.3 Normal curves with 1 < 2 and 1 = 2

Figure 6.4 Normal curves with 1 = 2 and 1 < 2

Figure 6.5 Normal curves with 1 < 2 and 1 < 2

Theorem 6.2

Section 6.3

Areas under the Normal Curve

Figure 6.6 P(x1 < X < x2) = area of the shaded region

Figure 6.7 P(x1 < X < x2) for different normal curves

Definition 6.1

Figure 6.8 The original and transformed normal distributions

Figure 6.9 Areas for Example 6.2

Figure 6.11 Area for Example 6.4

Section 6.4

Applications of the Normal Distribution

Figure 6.17 Specifications for Example 6.10

Section 6.5

Normal Approximation to the Binomial

Theorem 6.3

Figure 6.22 Normal approximation of b(x; 15,0.4)

Figure 6.23 Normal approximation of b(x; 15, 0.4) and b(x; 15, 0.4)

Figure 6.24 Histogram for b(x; 6, 0.2)

Figure 6.25 Histogram for b(x; 15, 0.2)

Table 6.1 Normal Approximation and True Cumulative Binomial Probabilities

Section 6.6

Gamma and Exponential Distributions

Definition 6.2

Figure 6.28 Gamma distributions

Theorem 6.4

Corollary 6.1

Section 6.7

Chi-Squared Distributions

Theorem 6.5

Section 6.8

Beta Distribution

Definition 6.3

Theorem 6.6

Section 6.9

Lognormal Distribution

Figure 6.29 Lognormal distributions

Theorem 6.7

Section 6.10

Weibull Distribution (Optional)

Theorem 6.8

Figure 6.30 Weibull distributions ( = 1)

Section 6.11

Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 6 Some Continuous Probability...

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