'Binomial experiment' presentation slideshows

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Probability Distributions and Statistics

8. Probability Distributions and Statistics. Distributions of Random Variables Expected Value Variance and Standard Deviation Binomial Distribution Normal Distribution Applications of the Normal Distribution. Random Variable.

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Discrete Probability Distributions

Discrete Probability Distributions. Discrete vs. Continuous. Discrete A random variable (RV) that can take only certain values along an interval: Cars passing by a point Results of coin toss Students taking a class Continuous

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Slides by

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1 Convergent, 0 Divergent Convergent 150 Convergent 28.78787878 2 2091.946 3 |r| < 1 4 59th term 0.958 5 Example

1 Convergent, 0 Divergent Convergent 150 Convergent 28.78787878 2 2091.946 3 |r| < 1 4 59th term 0.958 5 Example a n = 3n 3 +3n 2n 6 28 terms 7 Example 5, 3.75, 2.8125, 2.109375,... 8 2097151 9 a 1 = 17 a n = a n-1 + 8 n > 1 10 22.5 miles 11 -50300 44408920906.885

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Introduction to Probability and Statistics Thirteenth Edition

Introduction to Probability and Statistics Thirteenth Edition. Chapter 5 Several Useful Discrete Distributions. Introduction. Discrete random variables take on only a finite or countably infinite number of values.

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Activity 6 - 8

Activity 6 - 8. How Many Boys (or Girls)?. The five children of Tsar Nicholas II, circa 1907; Romanov Collection, General Collection, Beinecke Rare Book and Manuscript Library, Yale University. Objectives. Recognize components of a binomial experiment Calculate binomial probabilities.

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5-3 Additional Properties of The Binomial Distribution

5-3 Additional Properties of The Binomial Distribution. Binomial Experiments. Also called Bernoulli Experiments

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8.1 The Binomial Distribution

8.1 The Binomial Distribution. What are the conditions for the Binomial Setting? What is the Binomial Distribution Notation? How do you compute the probability of a value of X using the Binomial Distribution? How do you find the means and standard deviation of the Binomial Distribution?.

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Binomial Probability Distribution 1. The experiment must have a fixed number of trials .

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4.2 Binomial Distributions

4.2 Binomial Distributions. Determining binomial experiments Finding binomial probabilities using binomial probability formula, technology, or tables Graph a binomial distribution Find mean, variance, and standard deviation. Binomial Experiment.

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STATISTICAL INFERENCE May be divided into two major areas

STATISTICAL INFERENCE May be divided into two major areas. PARAMETER ESTIMATION. HYPOTHESIS TESTING. POINT ESTIMATION. The decision making procedure about the hypothesis. INTERVAL ESTIMATION. CONFIDENCE. INTERVALS. MEANS. VARIANCES. PROPORTIONS. POINT ESTIMATION.

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Categorical Data

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BINOMIAL DISTRIBUTIONS

BINOMIAL DISTRIBUTIONS.

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Use the Binomial Theorem to expand a binomial raised to a power.

Objectives. Use the Binomial Theorem to expand a binomial raised to a power. Find binomial probabilities and test hypotheses. Vocabulary. Binomial Theorem binomial experiment binomial probability.

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6.10 - The Binomial Distribution, Mean, and Standard Deviation

6.10 - The Binomial Distribution, Mean, and Standard Deviation. IBHL/SL - Santowski. (A) Review - Assumptions. Recall the various key points that we have previously made for binomial probabilities: (i) we have a fixed number of trials

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7.3 Binomial Trials

7.3 Binomial Trials . Binomial Trials: Success/Failure Probability of k Successes. Binomial Trials: Success/Failure. An experiment with just 2 outcomes is called a binomial trial (or Bernoulli trial. ) One outcome is labeled a success and the other is labeled a failure.

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11.6 Binomial Distribution

11.6 Binomial Distribution. 9.4.3.9 Use the relationship between conditional probabilities and relative frequencies in contingency tables. . Guiding Question: How can I use binomial expansion to explain probability?. Recall expanding binomials What does this look like?.

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5.1 Sampling Distributions for Counts and Proportions

5.1 Sampling Distributions for Counts and Proportions. Binomial Experiments. Consider the familiar discrete probability distribution of a fair 6-sided die. Now suppose we are interested in obtaining a 3 or a 4 over 100 rolls. Here we say that n=100 and p=1/3.

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