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Titlebook: Elementary Probability Theory; Melvin Hausner Book 1995 Melvin Hausner 1995 Probability theory.Random variable.binomial distribution.condi

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發(fā)表于 2025-3-21 16:07:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Elementary Probability Theory
編輯Melvin Hausner
視頻videohttp://file.papertrans.cn/308/307412/307412.mp4
圖書(shū)封面Titlebook: Elementary Probability Theory;  Melvin Hausner Book 1995 Melvin Hausner 1995 Probability theory.Random variable.binomial distribution.condi
描述This text contains ample material for a one term precalculus introduction to probability theory. lt can be used by itself as an elementary introduc- tion to probability, or as the probability half of a one-year probability- statistics course. Although the development of the subject is rigorous, experimental motivation is maintained throughout the text. Also, statistical and practical applications are given throughout. The core of the text consists of the unstarred sections, most of chapters 1-3 and 5-7. Included are finite probability spaces, com- binatorics, set theory, independence and conditional probability, random variables, Chebyshev‘s theorem, the law of large numbers, the binomial distribution, the normal distribution and the normal approxi- mation to the binomial distribution. The starred sections include limiting and infinite processes, a mathematical discussion of symmetry, and game theory. These sections are indicated with an*, and are optional and sometimes more difficult. I have, in most places throughout the text, given decimal equivalents to fractional answers. Thus, while the mathematician finds the answer p = 17/143 satisfactory, the scientist is best appeased by
出版日期Book 1995
關(guān)鍵詞Probability theory; Random variable; binomial distribution; conditional probability; law of large number
版次1
doihttps://doi.org/10.1007/978-1-4615-1753-5
isbn_softcover978-1-4613-5706-3
isbn_ebook978-1-4615-1753-5
copyrightMelvin Hausner 1995
The information of publication is updating

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Standard Deviation, same mean .,. It is seen (intuitively) that the “spread” of ..(.) is greater than that of ..(.), which in turn has a greater spread than ..(.). In the next section we shall learn how to measure this spread (standard deviation) exactly.
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Counting,, then it is not necessary to count, one by one, how many squares are needed to tile the room. The answer, of course, is 12 × 13 = 156 tiles. In this chapter we shall learn several short cuts of this type and apply them to probability problems.
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Category J Psychosexual Problems will agree (before the event occurs) that there is an element of uncertainty to the answer. An astonishing feature of probability theory is that it is possible to have such a theory at all. Yet this theory is not only possible, it is also one of the most interesting and fruitful theories of pure and applied mathematics.
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https://doi.org/10.1007/978-3-030-83317-610 if both dice are 6 and he wins $1.00 if only 1 of the dice is 6. But he loses $1.00 if no 6 appears. Here the amount . of . in dollars is a random variable. The value of . can be 10, 1, or ?1, depending on the outcome of the experiment.
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Mary Gene Saudelli,Jennifer Rowsell same mean .,. It is seen (intuitively) that the “spread” of ..(.) is greater than that of ..(.), which in turn has a greater spread than ..(.). In the next section we shall learn how to measure this spread (standard deviation) exactly.
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