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Con dence Interval on the Variance and Standard Deviation of a Normal Distribution 261 A Large-Sample Con dence Interval for a Population Proportion 265 A Prediction Interval for a Future Observation 268 Tolerance Intervals for a Normal Distribution 270

9-5.1 Large-Sample Tests on a Proportion 310 9-5.2 Small-Sample Tests on a Proportion (CD Only) 312 9-5.3 Type II Error and Choice of Sample Size 312

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9-6 9-7 9-8

9-1.1 Statistical Hypotheses 278 9-1.2 Tests of Statistical Hypotheses 280 9-1.3 One-Sided and Two-Sided Hypotheses 286 9-1.4 General Procedure for Hypothesis Testing 287

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Summary of Inference Procedures for a Single Sample 315 Testing for Goodness of Fit 315 Contingency Table Tests 320

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10-1 Introduction 328 10-2 Inference For a Difference in Means of Two Normal Distributions, Variances Known 328

10-2.1 Hypothesis Tests for a Difference in Means, Variances Known 329 10-2.2 Choice of Sample Size 331 10-2.3 Identifying Cause and Effect 333 10-2.4 Con dence Interval on a Difference in Means, Variances Known 334

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9-2.1 Hypothesis Tests on the Mean 289 9-2.2 P-Values in Hypothesis Tests 292 9-2.3 Connection Between Hypothesis Tests and Con dence Intervals 293 9-2.4 Type II Error and Choice of Sample Size 293 9-2.5 Large Sample Test 297 9-2.6 Some Practical Comments on Hypothesis Tests 298

33 33 33 34

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Throughout s 6 through 15, decisions are based statistical inference from sample data. Continuous probability models, speci cally the normal distribution, are used extensively to quantify the risks in these decisions and to evaluate ways to collect the data and how large a sample should be selected. IMPORTANT TERMS AND CONCEPTS In the E-book, click on any term or concept below to go to that subject. Analytic study Designed experiment Empirical model Engineering method Enumerative study Mechanistic model Observational study Overcontrol Population Probability model Problem-solving method Retrospective study Sample Statistical inference Statistical Process Control Statistical thinking Tampering Variability CD MATERIAL Factorial Experiment Fractional factorial experiment Interaction

E 110

1-2.5 A Factorial Experiment for the Connector Pull-off Force Problem (CD only)

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Much of what we know in the engineering and physical-chemical sciences is developed through testing or experimentation Often engineers work in problem areas in which no scienti c or engineering theory is directly or completely applicable, so experimentation and observation of the resulting data constitute the only way that the problem can be solved Even when there is a good underlying scienti c theory that we may rely on to explain the phenomena of interest, it is almost always necessary to conduct tests or experiments to con rm that the theory is indeed operative in the situation or environment in which it is being applied We have observed that statistical thinking and statistical methods play an important role in planning, conducting, and analyzing the data from engineering experiments To further illustrate the factorial design concept introduced in Section 1-2.

4, suppose that in the connector wall thickness example, there are two additional factors of interest, time and temperature The cure times of interest are 1 and 24 hours and the temperature levels are 70 F and 100 F Now since all three factors have two levels, a factorial experiment would consist of the eight test combinations shown at the corners of the cube in Fig S1-1 Two trials, or replicates, would be performed at each corner, resulting in a 16-run factorial experiment The observed values of pull-off force are shown in parentheses at the cube corners in Fig S1-1 Notice that this experiment uses eight 3 32-inch prototypes and eight 1 8-inch prototypes, the same number used in the simple comparative study in Section 1-1, but we are now investigating three factors Generally, factorial experiments are the most ef cient way to study the joint effects of several factors.

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