Fka twigs lp1 zippyshare. Probability and Statistics for Engineering and the Sciences, 8th Edition Pdf now you can download for free. How to download video from google drive to iphone. This detailed introduction to probability and statistics can provide you the solid grounding you want no matter what your technology specialization. Ang y Tang ProbabilityConceotinEngineering.pdf - Ebook download as PDF File (.pdf) or read book online. H-S, Probability Concepts in Engineering Planning and Design, 1984. Dynamics of Structures- 2nd Edition (j l Humar ). Rock candy xbox360 controller driver. Download the Book:Problems In Probability 2nd Edition PDF For Free, Preface: An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. How to change windows 7 iso download to 64 bit. The 2nd edition is a substantial revision of the 1.
Probability Concepts In Engineering 2nd Edition Pdf Download Free
This work thoroughly covers the concepts and main results of probability theory, from its fundamental principles to advanced applications. This edition provides examples early in the text of practical problems such as the safety of a piece of engineering equipment or the inevitability of wrong conclusions in seemingly accurate medical tests for AIDS and cancer.;College or university bookstores may order five or more copies at a special student price which is available upon request from Marcel Dekker, Inc.
Probability Concepts In Engineering 2nd Edition Pdf Download Windows 10
Apply the principles of probability and statistics to realistic engineering problems The easiest and most effective way to learn the principles of probabilistic modeling and statistical inference is to apply those principles to a variety of applications. That's why Ang and Tang's Second Edition of Probability Concepts in Engineering (previously titled Probability Concepts in Engineering Planning and Design) explains concepts and methods using a wide range of problems related to engineering and the physical sciences, particularly civil and environmental engineering. Now extensively revised with new illustrative problems and new and expanded topics, this Second Edition will help you develop a thorough understanding of probability and statistics and the ability to formulate and solve real-world problems in engineering. The authors present each basic principle using different examples, and give you the opportunity to enhance your understanding with practice problems. The text is ideally suited for students, as well as those wishing to learn and apply the principles and tools of statistics and probability through self-study. Key Features in this 2nd Edition: * A new chapter (Chapter 5) covers Computer-Based Numerical and Simulation Methods in Probability, to extend and expand the analytical methods to more complex engineering problems. * New and expanded coverage includes distribution of extreme values (Chapter 3), the Anderson-Darling method for goodness-of-fit test (Chapter 6), hypothesis testing (Chapter 6), the determination of confidence intervals in linear regression (Chapter 8), and Bayesian regression and correlation analyses (Chapter 9). * Many new exercise problems in each chapter help you develop a working knowledge of concepts and methods. * Provides a wide variety of examples, including many new to this edition, to help you learn and understand specific concepts. * Illustrates the formulation and solution of engineering-type probabilistic problems through computer-based methods, including developing computer codes using commercial software such as MATLAB and MATHCAD. * Introduces and develops analytical probabilistic models and shows how to formulate engineering problems under uncertainty, and provides the fundamentals for quantitative risk assessment. Sample questions asked in the 2nd edition of Probability Concepts in Engineering: Drinking water may be contaminated by two pollutants. In a given community, the probability of its drinking water containing excessive amount of pollutant A is 0.1, whereas that of pollutant B is 0.2. When pollutant A is excessive, it will definitely cause health problems; however, when pollutant B is excessive, it will cause health problems in only 20% of the population who has low natural resistance to that pollutant. Also, data from many similar communities reveal that the presence of these two pollutants in drinking water is not independent; half of those communities whose drinking water contain excessive amounts of pollutant A will also contain excessive amounts of pollutant B. Suppose a resident is selected at random from this community, what is the probability that he or she will suffer health problems from drinking the water? Assume that a person’s resistance to pollutant B is innate, which is independent of the event of having excessive pollutant in the drinking water. Data for the observed settlements of piles and the corresponding calculated settlements are compiled in Table E8.3 of Chapter 8. Based on these data, we can calculate the ratios of the observed to the corresponding calculated settlements; the results are as follows: Ratios of Observed Settlement to Calculated Settlement 0.12 0.97 0.86 1.14 0.94 2.37 0.88 0.92 1.01 0.99 1.02 1.04 0.99 0.87 0.52 0.94 1.06 1.38 1.04 1.18 1.00 0.86 0.82 0.84 1.09 The ratio of the observed to the calculated settlements is a measure of the accuracy of the calculational method. From the above data, we can observe that this ratio has considerable variability. (a) Assuming that the ratio is a Gaussian random variable, plot the above data on a normal probability paper and observe if there is a linear trend of the data points. (b) If a linear trend is observed, draw a straight line through the data points and estimate the mean and standard deviation from the straight line. Perform a chi-square goodness-of-fit test for the normal distribution at the 5% significance level. Also, do the same with the Anderson–Darling test. (c) Otherwise, if no linear trend can be observed in Part (b), plot the same data on another probability paper, such as the lognormal paper, and determine the suitability of this alternative distribution to model the relevant ratio, including a goodness-of-fit test at the 2% significance level to verify its suitability. TABLE E8.3 Summary of Data and Calculations for Example 8.3 EXAMPLE 8.3 Table E8.3 shows a set of data of observed settlements of pile groups (Col. 3), reported by Viggiani (2001), under the respective loads; also shown in the same table (Col. 4) are the corresponding calculated settlements using a nonlinear model proposed by Viggiani (2001). We may perform the regression of the observed settlement, Y , on the calculated settlement, X ; the calculations are summarized in Table E8.3. From Table E8.3, we obtain the sample means and sample standard deviations of Y and X , respectively, as follows: ? According to Eqs. 8.3 and 8.4, we obtain the corresponding regression coefficients for the regression of Y on X as follows: ? The data given in Columns 3 and 4 of Table E8.3 are plotted in Fig. E8.3. Therefore, the linear regression equation of Y on X is ? Figure E8.3 Data points and regression of observed settlement on calculated settlement. and with Eq. 8.9, we obtain the correlation coefficient ? which shows a very high correlation between the observed and calculated settlements. The conditional standard deviation of Y given X , according to Eq. 8.6a, is ? We may observe that this S Y|x is much smaller than the unconditional standard deviation s Y = 37.44 mm. We may also construct the 95% confidence interval of the regression line following Eq. 8.8. For this purpose, we first evaluate the corresponding 95% confidence intervals at the following selected discrete values of x i : 25 mm, 50 mm, 100 mm, 150 mm, and 180 mm. From Table A.3, t 0.975,23 = 2.069. ? By connecting two lines through the respective lower-bound and upper-bound values, as calculated above, we obtain the 95% confidence interval of the regression line as shown in dash lines in Fig. E8.3. TABLE A.3 Critical Values of t -Distribution at Confidence Level (1 ? ? ) = p d.o.f. p = 0.900 p = 0.950 p = 0.975 p = 0.990 p = 0.995 p = 0.999 1 3.0777 6.3138 12.7062 31.8205 63.6567 318.3088 2 1.8856 2.9200 4.3027 6.9646 9.9248 22.3271 3 1.6377 2.3534 3.1824 4.5407 5.8409 10.2145 4 1.5332 2.1318 2.7764 3.7469 4.6041 7.1732 5 1.4759 2.0150 2.5706 3.3649 4.0321 5.8934 6 1.4398 1.9432 2.4469 3.1427 3.7074 5.2076 7 1.4149 1.8946 2.3646 2.9980 3.4995 4.7853 8 1.3968 1.8595 2.3060 2.8965 3.3554 4.5008 9 1.3803 1.8331 2.2622 2.8214 3.2498 4.2968 10 1.3722 1.8125 2.2281 2.7638 3.1693 4.1437 11 1.3634 1.7959 2.2001 2.7181 3.1058 4.0247 12 1.3562 1.7823 2.1788 2.6810 3.0545 3.9296 13 1.3502 1.7709 2.1604 2.6503 3.0123 3.8520 14 1.3450 1.7613 2.1448 2.6245 2.9768 3.7874 15 1.3406 1.7531 2.1314 2.6025 2.9467 3.7328 16 1.3368 1.7459 2.1199 2.5835 2.9208 3.6862 17 1.3334 1.7396 2.1098 2.5669 2.8982 3.6458 18 1.3304 1.7341 2.1009 2.5524 2.8784 3.6105 19 1.3277 1.7291 2.0930 2.5395 2.8609 3.5794 20 1.3253 1.7247 2.0860 2.5280 2.8453 3.5518 21 1.3232 1.7207 2.0796 2.5176 2.8314 3.5272 22 1.3212 1.7171 2.0739 2.5083 2.8188 3.5050 23 1.3195 1.7139 2.0687 2.4999 2.8073 3.4850 24 1.3178 1.7109 2.0639 2.4922 2.7969 3.4668 25 1.3163 1.7081 2.0595 2.4851 2.7874 3.4502 26 1.3150 1.7056 2.0555 2.4786 2.7787 3.4350 27 1.3137 1.7033 2.0518 2.4727 2.7707 3.4210 28 1.3125 1.7011 2.0484 2.4671 2.7633 3.4082 29 1.3114 1.6991 2.0452 2.4620 2.7564 3.3962 30 1.3104 1.6973 2.0423 2.4573 2.7500 3.3852 31 1.3095 1.6955 2.0395 2.4528 2.7440 3.3749 32 1.3086 1.6939 2.0369 2.4487 2.7385 3.3653 33 1.3077 1.6924 2.0345 2.4448 2.7333 3.3563 34 1.3070 1.6909 2.0322 2.4411 2.7284 3.3479 35 1.3062 1.6896 2.0301 2.4377 2.7238 3.3400 36 1.3055 1.6883 2.0281 2.4345 2.7195 3.3326 37 1.3049 1.6871 2.0262 2.4314 2.7154 3.3256 38 1.3042 1.6806 2.0244 2.4286 2.7116 3.3190 39 1.3036 1.6849 2.0227 2.4258 2.7079 3.3128 40 1.3031 1.6839 2.0211 2.4233 2.7045 3.3069 45 1.3006 1.6794 2.0141 2.4121 2.6896 3.2815 50 1.2987 1.6759 2.0086 2.4033 2.6778 3.2614 55 1.2971 1.6703 2.0040 2.3961 2.6682 3.2451 60 1.2958 1.6706 2.0003 2.3901 2.6603 3.2317 70 1.2938 1.6794 1.9944 2.3808 2.6479 3.2108 80 1.2922 1.6759 1.9901 2.3739 2.6387 3.1953 90 1.2910 1.6750 1.9867 2.3685 2.6316 3.1833 ? 1.2824 1.6449 1.9600 2.3264 2.5759 3.0903 A contractor submits bids to three highway jobs and two building jobs. The probability of winning each job is 0.6. Assume that winning among the jobs is statistically independent. (a) What is the probability that the contractor will win at most one job? (b) What is the probability that the contractor will win at least two jobs? (c) What is the probability that he or she will win exactly one highway job, but none of the building jobs?