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# Probability Basic Probability Concepts Probability Distributions Sampling Distributions.

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• Slide 1
• Probability Basic Probability Concepts Probability Distributions Sampling Distributions
• Slide 2
• Probability Basic Probability Concepts
• Slide 3
• Basic Probability Concepts Probability refers to the relative chance that an event will occur. It represents a means to measure and quantify uncertainty. 0 probability 1
• Slide 4
• Basic Probability Concepts The Classical Interpretation of Probability: P(event) = # of outcomes in the event # of outcomes in sample space
• Slide 5
• Example: P(selecting a red card from deck of cards) ? Sample Space, S = all cards Event, E = red card then P(E) = # outcomes in E = 26 = 1 # outcomes in S 52 2
• Slide 6
• Probability Random Variables and Probability Distributions
• Slide 7
• Random Variable A variable that varies in value by chance
• Slide 8
• Random Variables Discrete variable - takes on a finite, countable # of values Continuous variable - takes on an infinite # of values
• Slide 9
• Probability Distribution A listing of all possible values of the random variable, together with their associated probabilities.
• Slide 10
• Notation: Let X = defined random variable of interest x = possible values of X P(X=x) = probability that takes the value x
• Slide 11
• Example: Experiment: Toss a coin 2 times. Of interest: # of heads that show
• Slide 12
• Example: Let X = # of heads in 2 tosses of a coin (discrete) The probability distribution of X, presented in tabular form, is: xP(X=x) 0.25 1.50 2.25 1.00
• Slide 13
• Methods for Establishing Probabilities Empirical Method Subjective Method Theoretical Method
• Slide 14
• Example: Toss 1 Toss 2 T T There are 4 possible T H outcomes in the H T sample space in this H H experiment
• Slide 15
• Example: Toss 1 Toss 2 T T P(X=0) = ? T H Let E = 0 heads in 2 tosses H T P(E) = # outcomes in E H H # outcomes in S = 1/4
• Slide 16
• Example: Toss 1 Toss 2 T T P(X=1) = ? T H Let E = 1 head in 2 tosses H T P(E) = # outcomes in E H H # outcomes in S = 2/4
• Slide 17
• Example: Toss 1 Toss 2 T T P(X=2) = ? T H Let E = 2 heads in 2 tosses H T P(E) = # outcomes in E H H # outcomes in S = 1/4
• Slide 18
• Example: The probability distribution in tabular form: xP(X=x) 0.25 1.50 2.25 1.00
• Slide 19
• Example: The probability distribution in graphical form: P(X=x)1.00.75.50.25 012 x
• Slide 20
• Probability distribution, numerical summary form: Measure of Central Tendency: mean = expected value Measures of Dispersion: variance standard deviation Numerical Summary Measures
• Slide 21
• Expected Value Let = E(X) = mean = expected value then = E(X) = x P(X=x)
• Slide 22
• Example: xP(X=x) 0.25 1.50 2.25 1.00 = E(X) = 0(.25) + 1(.50) + 2(.25) = 1
• Slide 23
• Variance Let = variance then = ( x - ) P(X=x)
• Slide 24
• Standard Deviation Let = standard deviation then =
• Slide 25
• Example: xP(X=x) 0.25 1.50 2.25 1.00 = (0-1)(.25) + (1-1)(.50) + (2-1)(.25) =.5 = .5 =.707
• Slide 26
• Practical Application Risk Assessment: Investment AInvestment B E(X) E(X) Choice of investment the investment that yields the highest expected return and the lowest risk.
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