IB SLHL Binomial probabilities

Dec 21, 2011

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IB SLHL Binomial probabilities

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Probabilities

Probabilities. Random Number Generators Actually pseudo-random Seed Same sequence from same seed Often time is used. Many examples on web. Custom random number generators exist. Can be used in algorithms. Actions Based on Probabilities. Assign probabilities to each action.

283 views • 13 slides

Binomial Probabilities

Binomial Probabilities. IBHL, Y2 - Santowski. (A) Coin Tossing Example. Take 2 coins and toss each Make a list to predict the possible outcomes Determine the ratio in which the possible outcomes (# of heads) occur Now repeat for 3 coins And repeat again for 4 coins

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Finding Probabilities

Finding Probabilities. Sec 5.2 Part one. Vocabulary: . Sample Space : For a random Phenomenon the sample space is the set of all possible outcomes. Examples: For the flip of two coins the sample space is. {(HH), (HT), (TH), (TT)} A single role of a six sided die.

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Binomial Project

Binomial Project. Excellent Students #1 and #2. We decided to chose recycling as our topic because we were curious to see if CCA students cared about our environment.

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Expected Probabilities

Expected Probabilities. Slideshow 50, Mathematics Mr Richard Sasaki, Room 307. Understand how expected probabilities are created Use notation to show such expected probabilities. Objectives. Vocabulary. We need to learn or review some vocabulary.

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Probabilities

Probabilities. Probabilities. Basic concepts : Random experiment : All possible outcomes have to be known in advance The result of a particular experimen tcannot be predicted ( randomness ). The experiment can be repeated under identical conditions Sample space Ω :

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Limiting probabilities

Limiting probabilities. When do the limiting probabilities exist?. The limiting probabilities P j exist if (a) all states of the Markov chain communicate (i.e., starting in state i , there is a positive probability of ever being in state j , for all i , j and

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Adding Probabilities

Adding Probabilities. Advanced Math Topics. Vocabulary. Simple Event: cannot be broken down into smaller events Rolling a 1 on a 6 sided die Compound Event: can be broken down into smaller events Rolling an odd number on a 6 sided die

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Probabilities

Probabilities. Introduction. Human life is full of uncertainties. In our day to day life very often we make guess & use statements like :”possibility of a particular party to win this election is more “,”Most probably it will rain to-day “. Analizing some real life situations.

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Discrete Probabilities

Discrete Probabilities. CS5303 – Logical Foundations of Computer Science. Applications of probabilities to CS. Algorithms (average complexity) Probabilistic Algorithms Modeling and Simulation Queuing processes Signal processing. Definition.

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For which of the following probabilities would the binomial distribution be appropriate?

Why does a distribution of Z-scores have a mean of 0 and a standard deviation of 1? Give the most persuasive answer. (A) That property follows solely from the formula for Z-score. (B) It follows from the formula for Z-score and properties of expected value.

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Counting Rules and Binomial Probabilities

Counting Rules and Binomial Probabilities. Stat 509 Lecture (E. Pena) February 12, 2001. Fundamental Theorem of Counting. Suppose you have an experiment which could be performed in k steps, so the outcome of this experiment will be of form:. (O 1 , O 2 , …, O k ).

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GENETIC PROBABILITIES

GENETIC PROBABILITIES. SEX CHROMOSOMES. TYPES OF CHROMOSOMES. Just like our cells are categorized as either normal body cells (somatic cells), our chromosomes are categorized the same way. TYPES OF CHROMOSOMES.

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Statistics Probabilities

Statistics Probabilities. Chapter 4 Example Problems. Probability Rules. Probabilities must be between 0 and 1 Where 0 is impossible Where 1 is certain No negative probabilities All probabilities must add to 1. Finding Probabilities.

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Probabilities

Probabilities. Addition Rule: p(A or B) = p(A) + p(B) - p(A&B). A. B. A & B. Probabilities. Addition Rule: p(A or B) = p(A) + p(B) - p(A&B). A. B. A & B. Probabilities. Multiplication Rule: p(A & B) = p(A) · p(B|A). Calculate: p(Liar), p(Lawyer), p(Liar | Lawyer), p(Lawyer | Liar)

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Genetic Probabilities

Genetic Probabilities. Learning Objectives. By the end of this class you should understand: The purpose and nature of dihybrid crosses How to calculate the probability that an unaffected person may be a carrier for a disorder What a rare-allele assumption is for

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ANATOMY OF BINOMIAL PROBABILITIES

ANATOMY OF BINOMIAL PROBABILITIES. A Binomial Distribution is a form of a discrete probability distribution having only two outcomes. A Binomial Probability is the result of an experiment conducted on a Binomial Distribution. These distributions have the following four characteristics:

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Calculating Probabilities

Calculating Probabilities. If a set of events have an equal chance of occurring, it is possible to calculate probabilities. For example, you can calculate the probability of rolling a 4 on a die because the 4 has the same chance of occurring as the other numbers. Calculating Probabilities.

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Poisson Probabilities

Poisson Probabilities. Click Insert Function ( f x ) Select Statistical as your function category Choose POISSON.DIST. 1. 2. Poisson Probabilities. You must enter X = Number of occurrences of interest Mean = Expected number of occurrences

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Objective -To find binomial probabilities and analyze binomial distributions.

Objective -To find binomial probabilities and analyze binomial distributions. Note: Each trial must have 2 possible outcomes. Calculate the probability of tossing a coin 15 times and getting three heads?. Calculate the probability of randomly guessing all the

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Bayesian Probabilities

Bayesian Probabilities. Recall that Bayes theorem gives us a simple way to compute likelihoods for hypotheses and thus is useful for problems like diagnosis, recognition, interpretation, etc P(h | E) = p(E | h) * P(h) As we discussed, there are several problems when applying Bayes

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Multiplying Probabilities

Multiplying Probabilities. Advanced Math Topics. Probability of Two Independent Events. If two events A and B are independent then the probability of both events occurring is P(A and B) = P(A) ∙ P(B) This can be applied to any number of independent events!. Example 1.

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