265 Mathematical Functions with Computer Software 266 Chapter 7: Probability Analysis for Computing Engineers 267 1-1 Probability Distribution Function 267 1-2 Cumulative Probability 268 2-1 The Uniform Distribution 268 2-2 The Bias of the Random Sample Mean . 270 Inaccuracy of the Population Mean and Sample Mean 270 3-1 The Central Limit Theorem 270 3-2 The Central Limit Theorem 270 3-3 The R-Squared Correlation Coefficient 272 3-4 The Maximum Likelihood Estimator . 275 3-5 A Bayesian Estimator . 276 3-6 Coverage . 278 3-7 Distribution of . 283 3-8 Assessing the Accuracy of the Mean . 285 3-9 The Standard Error . 290 3-10 Fitting the Sample Mean to a Normal Distribution 292 3-11 The Percent Confidence Interval for the Mean . 294 3-12 Variance and Standard Error of the Mean . 298 3-13 The Beta Distribution . 300 3-14 The Binomial Distribution . 304 3-15 The Poisson Distribution . 307 3-16 The Gamma Distribution . 309 3-17 The Negative Binomial Distribution . 312 3-18 The Other Important Distributions . 314 3-19 The Cumulative Distribution Function . 316 3-20 Sample Size Calculations for . 322 3-21 Simulation Studies . 329 3-22 Applying the Normal Distribution . 331 3-23 One-Sided Testing . 335 3-24 Two-Sided Testing . 336 3-25 Applying the Uniform Distribution . 338 3-26 Applying the Beta Distribution . 342 3-27 Applying the Poisson Distribution . 343 3-28 Applying the Gamma Distribution . 344 3-29 The Importance of . 347 3-30 The Importance of Sample Size in Regression Analysis . 349 3-31 The Importance of . 354 4-1 The Normal Distribution . 356 4-2 Mean and Variance . 358 4-3 The Normal Distribution of a Continuous Variable 359 4-4 The Normal Distribution of a Discrete Variable . 361 4-5 The Normal Distribution:
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