'Equally likely' presentation slideshows

Part I: Crypto

Part I: Crypto. Chapter 2: Crypto Basics. MXDXBVTZWVMXNSPBQXLIMSCCSGXSCJXBOVQXCJZMOJZCVC TVWJCZAAXZBCSSCJXBQCJZCOJZCNSPOXBXSBTVWJC JZDXGXXMOZQMSCSCJXBOVQXCJZMOJZCNSPJZHGXXMOSPLH JZDXZAAXZBXHCSCJXTCSGXSCJXBOVQX ? plaintext from Lewis Carroll, Alice in Wonderland

3.37k views • 317 slides

Part I: Crypto

Part I: Crypto. Chapter 2: Crypto Basics. MXDXBVTZWVMXNSPBQXLIMSCCSGXSCJXBOVQXCJZMOJZCVC TVWJCZAAXZBCSSCJXBQCJZCOJZCNSPOXBXSBTVWJC JZDXGXXMOZQMSCSCJXBOVQXCJZMOJZCNSPJZHGXXMOSPLH JZDXZAAXZBXHCSCJXTCSGXSCJXBOVQX ? plaintext from Lewis Carroll, Alice in Wonderland

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Comparing groups

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Welcome to BUAD 310

Welcome to BUAD 310. Instructor: Kam Hamidieh Lecture 4, Monday January 27, 2014. Agenda & Announcement. Today : Chapter 6 (Only Sections 6.1 & 6.2, will say more when we get to linear regression.) Read pages 105-110. Chapters 7. Read all of it! Note:

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Random copolymer adsorption

Random copolymer adsorption. Juan Alvarez Department of Mathematics and Statistics, University of Saskatchewan. E. Orlandini, Dipartimento di Fisica and Sezione CNR-INFM, Universit`a di Padova C.E. Soteros, Department of Mathematics and Statistics, University of Saskatchewan

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Chapter 5 Continuous Random Variables

Chapter 5 Continuous Random Variables. 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution 5.3 The Normal Probability Distribution. Continuous Probability Distributions.

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Statistics Review for CM 160/260

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Probability

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Problem 1: Relationship between Two Variables-1 (1)

Problem 1: Relationship between Two Variables-1 (1) First, identify two variables with which this question is related. Here, the variables of interest are [attend] and [sex].

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Problem 1: Relationship between Two Variables-1 (1)

Problem 1: Relationship between Two Variables-1 (1). First, identify two variables with which this question is related. Here, the variables of interest are [attend] and [sex]. .

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Section 6.2.2 Probability Models

Section 6.2.2 Probability Models. AP Statistics November 25, 2009 Berkley High School todd1@toddfadoir.com. Notation. Probability Rules: Rule 1. Probability Rules: Rule 2. Probability Rules: Rule 3. Probability Rules: Rule 4 . Probability Rules: Rule 4 (Different Notation) . Example.

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Vocabulary Probability: the chances of an event will happen Certain: the event will definitely happen

Vocabulary Probability: the chances of an event will happen Certain: the event will definitely happen. What are the chances of pulling a red marble?. It is certain that you will pull a red marble because they are all red!. Impossible: the event will not happen.

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Class 06

Class 06. 07. Case: The Roulette Wheel Goodness of Fit Tests. EMBS 11.2. What we learned last class. Probability Distributions have characteristics Descriptive Statistics are used to estimate those characteristics. Location (mean, median, mode)

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Chapter 4 Probability

Chapter 4 Probability. The description of sample data is only a preliminary part of a statistical analysis. A major goal is to make generalizations or inferences about the target population on the basis of the information obtained from the sample data.

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An example of the benefits of diversification

An example of the benefits of diversification. Setup. There are two assets Ebay IBM There are two equally likely states of the world State 1 (occurs with probability 1/2) State 2 (occurs with probability 1/2). Returns.

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Information Units of Measurement

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Information Theory

Information Theory. Rusty Nyffler. Introduction. Entropy Example of Use Perfect Cryptosystems Common Entropy. Entropy. Measure of uncertainty Entropy of a fair coin toss = 1. Why is entropy important?.

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Lesson 2.6- Simple Probability and Odds, pg. 96

Lesson 2.6- Simple Probability and Odds, pg. 96. Objectives: To find the probability of a simple event. To find the odds of a simple event. Vocabulary. Probability : the ratio of the number of favorable outcomes for an event to the number of possible outcomes of the event.

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CONDITIONAL PROBABILITY

CONDITIONAL PROBABILITY. Consider two events, A and B. Suppose we know that B has occurred. This knowledge may change the probability that A will occur. We denote by P(A|B) the conditional probability of event A given that B has occurred.

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2 3. P (landing between 2 and 10) = = , or . length of favorable segment length of entire segment. 8 12. Geometric Probability. LESSON 10-8. Additional Examples.

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