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MAFS.912.S-CP.1.3 - Understand the conditional probability of A
MAFS.912.S-CP.1.3 - Understand the conditional probability of A

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... possessing a normal distribution with a mean of 10.5 ounces and a standard deviation of .2 ounces. Suppose 100 bags of chips were randomly selected from this dispensing machine. Find the probability that the sample mean weight of these 100 bags exceeded 10.45 ounces. ...
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... Example: Spell-check software catches ”nonword errors” that result in a string of letters that is not a word, as when ”the” is typed as ”teh”. When undergraduates are asked to type a 250-word essay(without spell-checking), the number X of nonword errors has the following distribution: Value of X ...
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Chapter 4

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... Conditional Probabilities and Independen e Sin e we usually deal with more than one observation, the joint probabilities as in the expression (1) above are really important to us. In general, they may be di ult to assess, and knowing ea h of the individual probabilities is normally not enough. To a ...
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Lecture 5
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... • In some circumstances, we are willing to assume that individual outcomes are equally likely because of some balance in the phenomenon • Examples: – Ordinary coins have a physical balance that should make heads and tails equally likely – The table of random digits comes from a deliberate randomizat ...
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Probability

Probability is the measure of the likeliness that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty). The higher the probability of an event, the more certain we are that the event will occur. A simple example is the toss of a fair (unbiased) coin. Since the two outcomes are equally probable, the probability of ""heads"" equals the probability of ""tails"", so the probability is 1/2 (or 50%) chance of either ""heads"" or ""tails"".These concepts have been given an axiomatic mathematical formalization in probability theory (see probability axioms), which is used widely in such areas of study as mathematics, statistics, finance, gambling, science (in particular physics), artificial intelligence/machine learning, computer science, game theory, and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems.
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