File - Math with Ms. Plant
... affects the probability of the other event occurring. • Instead, we need to think about how the occurrence of one event will effect the sample space of the second event to determine the probability of the second event occurring. • Then we can multiply the new probabilities. ...
... affects the probability of the other event occurring. • Instead, we need to think about how the occurrence of one event will effect the sample space of the second event to determine the probability of the second event occurring. • Then we can multiply the new probabilities. ...
Mathematics Curriculum 7 Statistics and Probability
... compute probabilities in simple settings. They also learn how to estimate probabilities empirically. Probability provides a foundation for the inferential reasoning developed in the second half of this module. Additionally, students build on their knowledge of data distributions that they studied in ...
... compute probabilities in simple settings. They also learn how to estimate probabilities empirically. Probability provides a foundation for the inferential reasoning developed in the second half of this module. Additionally, students build on their knowledge of data distributions that they studied in ...
(1/2) 3 x
... The multinomial has many uses in genetics where a person may have 1 of many possible alleles (that occur with certain probabilities in a given population) at a gene locus. ...
... The multinomial has many uses in genetics where a person may have 1 of many possible alleles (that occur with certain probabilities in a given population) at a gene locus. ...
Probability — the language of randomness The field of statistics is
... result. Consider a sporting event that is divided into two halves, with the final score for a team being the total of the scores for each half. Examples of such events include soccer, football and basketball. Assume that the two teams are precisely evenly matched. Also, assume that the result of pla ...
... result. Consider a sporting event that is divided into two halves, with the final score for a team being the total of the scores for each half. Examples of such events include soccer, football and basketball. Assume that the two teams are precisely evenly matched. Also, assume that the result of pla ...
Inverse Probability
... values of 8 we may take "9 to be constant." Perhaps we might add that all values of 8 being equally possible their probabilities are by definition equal; but however we might disguise it, the choice of this particular a p7~iori distribution for the d's is just as arbitrary as any other could be. If ...
... values of 8 we may take "9 to be constant." Perhaps we might add that all values of 8 being equally possible their probabilities are by definition equal; but however we might disguise it, the choice of this particular a p7~iori distribution for the d's is just as arbitrary as any other could be. If ...
EGR 252 Chapter 3
... Chapter 3: Random Variables and Probability Distributions Definition and nomenclature A random variable is a function that associates a real number with each element in the sample space. We use an uppercasel letter such as X to denote the random variable. We use a lowercase letter such as x ...
... Chapter 3: Random Variables and Probability Distributions Definition and nomenclature A random variable is a function that associates a real number with each element in the sample space. We use an uppercasel letter such as X to denote the random variable. We use a lowercase letter such as x ...
Chapter 4 Key Ideas Events, Simple Events, Sample Space, Odds
... Q: Bob rolls a 6-sided die. What is the sample space of this procedure? A: S = {1, 2, 3, 4, 5, 6} Q: Sue measures how many coin flips it takes to get 3 heads. What is the sample space of this procedure? A: S = {3, 4, 5, …} = {all integers > 2} Q: Fred sees what proportion of cars on his block are SU ...
... Q: Bob rolls a 6-sided die. What is the sample space of this procedure? A: S = {1, 2, 3, 4, 5, 6} Q: Sue measures how many coin flips it takes to get 3 heads. What is the sample space of this procedure? A: S = {3, 4, 5, …} = {all integers > 2} Q: Fred sees what proportion of cars on his block are SU ...
Mutually Exclusive Events
... •For example, the probability of flipping a coin twice and the coin landing on heads the second time is not affected by (i.e. is independent of) whether the first coin flip turned up heads or tails. •P(A and B) = P(A) × P(B) ...
... •For example, the probability of flipping a coin twice and the coin landing on heads the second time is not affected by (i.e. is independent of) whether the first coin flip turned up heads or tails. •P(A and B) = P(A) × P(B) ...
May 25
... same whether or not A has occurred. If (and only if) A and B are independent, then P(B | A) = P(B | not A) = P(B) • For example, if I am tossing two coins, the probability that the second coin lands heads is always .50, whether or not the first coin ...
... same whether or not A has occurred. If (and only if) A and B are independent, then P(B | A) = P(B | not A) = P(B) • For example, if I am tossing two coins, the probability that the second coin lands heads is always .50, whether or not the first coin ...
Chapter 21 guided notes
... We hope that our decision will be correct, but it is possible that we make the wrong decision. There are two ways to make a wrong decision: ...
... We hope that our decision will be correct, but it is possible that we make the wrong decision. There are two ways to make a wrong decision: ...
notes
... outcomes, {H, H}, {H, T}, and {T, T}. • If the coins are different, or if they are thrown one after the other, there are four distinct outcomes: (H, H), (H, T), (T, H), (T, T), which are often presented in a more concise form: HH, HT, TH, TT. • Thus, depending on the nature of the experiment, there ...
... outcomes, {H, H}, {H, T}, and {T, T}. • If the coins are different, or if they are thrown one after the other, there are four distinct outcomes: (H, H), (H, T), (T, H), (T, T), which are often presented in a more concise form: HH, HT, TH, TT. • Thus, depending on the nature of the experiment, there ...
Ars Conjectandi
Ars Conjectandi (Latin for The Art of Conjecturing) is a book on combinatorics and mathematical probability written by Jakob Bernoulli and published in 1713, eight years after his death, by his nephew, Niklaus Bernoulli. The seminal work consolidated, apart from many combinatorial topics, many central ideas in probability theory, such as the very first version of the law of large numbers: indeed, it is widely regarded as the founding work of that subject. It also addressed problems that today are classified in the twelvefold way, and added to the subjects; consequently, it has been dubbed an important historical landmark in not only probability but all combinatorics by a plethora of mathematical historians. The importance of this early work had a large impact on both contemporary and later mathematicians; for example, Abraham de Moivre.Bernoulli wrote the text between 1684 and 1689, including the work of mathematicians such as Christiaan Huygens, Gerolamo Cardano, Pierre de Fermat, and Blaise Pascal. He incorporated fundamental combinatorial topics such as his theory of permutations and combinations—the aforementioned problems from the twelvefold way—as well as those more distantly connected to the burgeoning subject: the derivation and properties of the eponymous Bernoulli numbers, for instance. Core topics from probability, such as expected value, were also a significant portion of this important work.