TAILIEUCHUNG - Sách: Introduction To Probability

In this chapter, we shall first consider chance experiments with a finite number of possible outcomes ω1 , ω2 , . . . , ωn . For example, we roll a die and the possible outcomes are 1, 2, 3, 4, 5, 6 corresponding to the side that turns up. We toss a coin with possible outcomes H (heads) and T (tails). It is frequently useful to be able to refer to an outcome of an experiment. For example, we might want to write the mathematical expression which gives the sum of four rolls of a die. | Chapter 1 Discrete Probability Distributions Simulation of Discrete Probabilities Probability In this chapter we shall first consider chance experiments with a finite number of possible outcomes 1 2 . n. For example we roll a die and the possible outcomes are 1 2 3 4 5 6 corresponding to the side that turns up. We toss a coin with possible outcomes H heads and T tails . It is frequently useful to be able to refer to an outcome of an experiment. For example we might want to write the mathematical expression which gives the sum of four rolls of a die. To do this we could let Xi i 1 2 3 4 represent the values of the outcomes of the four rolls and then we could write the expression X1 X2 X3 X4 for the sum of the four rolls. The Xj s are called random variables. A random variable is simply an expression whose value is the outcome of a particular experiment. Just as in the case of other types of variables in mathematics random variables can take on different values. Let X be the random variable which represents the roll of one die. We shall assign probabilities to the possible outcomes of this experiment. We do this by assigning to each outcome j a nonnegative number m j in such a way that m i m 2 - m e 1 The function m j is called the distribution function of the random variable X. For the case of the roll of the die we would assign equal probabilities or probabilities 1 6 to each of the outcomes. With this assignment of probabilities one could write 2 P X 4 3 1 2 CHAPTER 1. DISCRETE PROBABILITY DISTRIBUTIONS to mean that the probability is 2 3 that a roll of a die will have a value which does not exceed 4. Let Y be the random variable which represents the toss of a coin. In this case there are two possible outcomes which we can label as H and T. Unless we have reason to suspect that the coin comes up one way more often than the other way it is natural to assign the probability of 1 2 to each of the two outcomes. In both of the above experiments each outcome is .

• Tiếng Anh thương mại
• statistical probability
• probability distributions
• probability densities
• combinatorics
• conditional probability
• Statistical techniques
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• Statistical techniques in economics
• Descriptive statistics
• Probability concepts
• Random experiment
• Discrete probability distributions
• Continuous random variables
• Probability distribution
• Continuous probability distributions
• Normal probability distribution
• Random observation
• Bayes’ theorem
• Business & economics
• Describing data
• Numerical measures
• Sampling methods
• Frequency distributions
• Graphic presentation
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• Lecture Probability & statistics
• Probability & statistics
• Tính toán và thông kê
• Hypothesis testing
• Statistical hypothesis
• Error types
• Probability and statistics for engineers
• Fundamentals of probability for engineers
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• Statistical inference
• Parameter estimation
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• Basic Probability
• Discrete Variables
• Distribution Functions
• Statistical Inferences
• Proportion
• Routine Operating Da
• Spinning Pointers
• Flipping Coins
• Probability Spaces
• Discrete Probability Spaces
• Random Variables
• Mixture Distributions
• Statistics for business
• Applied statistics and probability
• Simple linear regression
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• Nonparametric statistics
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• Several factors
• Lecture ELEC3540 analog and digital communications
• ELEC3540 analog and digital communications
• Random analysis
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• Entering data
• Palette swapping
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