Project Storm Fury Review A stochastic variable has the following probability distribution: Values of X Probability distribution of X xP(X=x) $1P(X=1)

Slides:



Advertisements
Similar presentations
Continuous Random Variables Chapter 5 Nutan S. Mishra Department of Mathematics and Statistics University of South Alabama.
Advertisements

Probabilistic models Haixu Tang School of Informatics.
McGraw-Hill/Irwin Copyright © 2004 by the McGraw-Hill Companies, Inc. All rights reserved. Chapter 3 Risk Identification and Measurement.
CS433: Modeling and Simulation
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
Continuous Random Variable (1). Discrete Random Variables Probability Mass Function (PMF)
Review of Basic Probability and Statistics
QUANTITATIVE DATA ANALYSIS
Environmentally Conscious Design & Manufacturing (ME592) Date: May 5, 2000 Slide:1 Environmentally Conscious Design & Manufacturing Class 25: Probability.
CHAPTER 6 Statistical Analysis of Experimental Data
Non-parametric Bayesian value of information analysis Aim: To inform the efficient allocation of research resources Objectives: To use all the available.
Chapter 5 Continuous Random Variables and Probability Distributions
CHAPTER 6 Random Variables
© 2010 Pearson Education Inc.Goldstein/Schneider/Lay/Asmar, CALCULUS AND ITS APPLICATIONS, 12e – Slide 1 of 15 Chapter 12 Probability and Calculus.
Prof. SankarReview of Random Process1 Probability Sample Space (S) –Collection of all possible outcomes of a random experiment Sample Point –Each outcome.
5-2 Probability Distributions This section introduces the important concept of a probability distribution, which gives the probability for each value of.
Chapter 3 Statistical Concepts.
T tests comparing two means t tests comparing two means.
Estimating parameters in a statistical model Likelihood and Maximum likelihood estimation Bayesian point estimates Maximum a posteriori point.
6.1B Standard deviation of discrete random variables continuous random variables AP Statistics.
Portfolio Theory Chapter 7
Chapter 5: The Binomial Probability Distribution and Related Topics Section 1: Introduction to Random Variables and Probability Distributions.
Mean and Standard Deviation of Discrete Random Variables.
Math b (Discrete) Random Variables, Binomial Distribution.
Lecture V Probability theory. Lecture questions Classical definition of probability Frequency probability Discrete variable and probability distribution.
Unit 8 Section 8-3 – Day : P-Value Method for Hypothesis Testing  Instead of giving an α value, some statistical situations might alternatively.
Aim: How do we use a t-test?
AGEC 608 Lecture 01, p. 1 AGEC 608: Lecture 1 Objective: Introduction to main concepts Readings: –Boardman, Chapter 1 –Kankakee, summary of Draft Assessment.
Topic 5 - Joint distributions and the CLT
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 6 Random Variables 6.1 Discrete and Continuous.
Random Variables Ch. 6. Flip a fair coin 4 times. List all the possible outcomes. Let X be the number of heads. A probability model describes the possible.
Review of Probability. Important Topics 1 Random Variables and Probability Distributions 2 Expected Values, Mean, and Variance 3 Two Random Variables.
T tests comparing two means t tests comparing two means.
© Copyright McGraw-Hill 2004
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 11 Analyzing the Association Between Categorical Variables Section 11.2 Testing Categorical.
T Test for Two Independent Samples. t test for two independent samples Basic Assumptions Independent samples are not paired with other observations Null.
AP STATISTICS Section 7.1 Random Variables. Objective: To be able to recognize discrete and continuous random variables and calculate probabilities using.
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
Continuous Random Variables and Probability Distributions
T tests comparing two means t tests comparing two means.
7.2 Means & Variances of Random Variables AP Statistics.
AP Stats Chapter 7 Review Nick Friedl, Patrick Donovan, Jay Dirienzo.
Random Variables By: 1.
Chapter 9 Hypothesis Testing
Probabilistic Cash Flow Analysis
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
Math a Discrete Random Variables
Random Variables and Probability Distribution (2)
STAT 312 Chapter 7 - Statistical Intervals Based on a Single Sample
Simulation Statistics
Random Variable.
Discrete and Continuous Random Variables
Chapter 5 STATISTICS (PART 1).
Means and Variances of Random Variables
The Hurricane: Nature’s Fury
Chapter 9: Hypothesis Tests Based on a Single Sample
Random Variable.
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
Analyzing the Association Between Categorical Variables
CHAPTER 6 Random Variables
Power Section 9.7.
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
CHAPTER 6 Random Variables
Part II: Discrete Random Variables
Inference Concepts 1-Sample Z-Tests.
Presentation transcript:

Project Storm Fury

Review A stochastic variable has the following probability distribution: Values of X Probability distribution of X xP(X=x) $1P(X=1) = 1/3 $2P(X=2) = 1/3 $3P(X=3) = 1/3

Review What is X’s cumulative probability distribution? What is its expected value (  X =?) What is the Variance of X? What is its standard deviation? What is X’s cumulative probability distribution? What is its expected value (  X =?) What is the Variance of X? What is its standard deviation?

Review What is the Variance of X? Var X =   x i 2 P(X=x i ) -  2 = [1(1/3) (1/3) (1/3)] = (1/3 + 4/3 + 3) - 4 = 2/3 What is its standard deviation (  )?  X = SqrRoot(Var X ) = (2/3) 1/2 =.8165 What is the Variance of X? Var X =   x i 2 P(X=x i ) -  2 = [1(1/3) (1/3) (1/3)] = (1/3 + 4/3 + 3) - 4 = 2/3 What is its standard deviation (  )?  X = SqrRoot(Var X ) = (2/3) 1/2 =.8165

Total Property Damage ($ of 1969)

Maximum sustained winds over time

Alternative Hypotheses H1, the “beneficial” hypothesis. The average effect of seeding is to reduce maximum sustained wind speed. H2, the “null” hypothesis. Seeding has no effect on hurricanes. No change is induced in maximum sustained wind speed. H3, the “detrimental” hypothesis. The average effect of seeding is to increase the maximum sustained wind speed. H1, the “beneficial” hypothesis. The average effect of seeding is to reduce maximum sustained wind speed. H2, the “null” hypothesis. Seeding has no effect on hurricanes. No change is induced in maximum sustained wind speed. H3, the “detrimental” hypothesis. The average effect of seeding is to increase the maximum sustained wind speed.

Mathematical expressions P(w' | H 2 ) = P(w) = f N (  100 ,  15.6  ) P(w' | H 1 ) = ƒ N (  85 ,  18.6  ) P(w' | H 3 ) = ƒ N (  110 ,  18.6  ) P(w' | H 2 ) = P(w) = f N (  100 ,  15.6  ) P(w' | H 1 ) = ƒ N (  85 ,  18.6  ) P(w' | H 3 ) = ƒ N (  110 ,  18.6  )

Probability density function for Debbie results P(69 , 85  | H1) = 1.50 x 2.14 =3.21 P(69 , 85  | H2) = x 1.64 = 0.61 P(69 , 85  | H3) = X = P(H1 | 69 , 85  ) = (3.21 x 1/3)/(3.21 x 1/ x 1/ x 1/3) =.81 P(H2 | 69 , 85  ) =.15 P(H3 | 69 , 85  ) =.04 P(69 , 85  | H1) = 1.50 x 2.14 =3.21 P(69 , 85  | H2) = x 1.64 = 0.61 P(69 , 85  | H3) = X = P(H1 | 69 , 85  ) = (3.21 x 1/3)/(3.21 x 1/ x 1/ x 1/3) =.81 P(H2 | 69 , 85  ) =.15 P(H3 | 69 , 85  ) =.04

Prior probabilities - pre and post Debbie P(H1) =.15 P(H2) =.75 P(H3) =.10 P(H1) =.15 P(H2) =.75 P(H3) =.10 P(H1) =.49 P(H2) =.49 P(H3) =.02.81(.15)/ [.81(.15) +.15(.75) +.04(.1)] =.51.15(.75)/ [.81(.15) +.15(.75) +.04(.1)] =.47.04(.1)/ [.81(.15) +.15(.75) +.04(.1)] =.02

The Seeding Decision

Probabilities assigned to wind changes occurring in the 12 hours before hurricane landfall Cumulative probability functions

Probabilities assigned to wind changes occurring in the 12 hours before hurricane landfall. Discrete approximation for five outcomes.

The seeding decision for the nominal hurricane $21.7M

The expected value of perfect information

The value of further tests

Review 1. Decide whose benefits and costs count, and how much. This is typically referred to as determining standing. 2. Select the portfolio of alternative initiatives. 3. Catalog potential consequences and select measurement indicators. 4. Predict quantitative consequences over the life of the project for those who have standing. 5. Monetize (attach cash values to) all the predicted consequences. 6. Discount for time to find present values. 7. Sum up benefits and Costs for each initiative and Perform sensitivity analysis underlying key assumptions 1. Decide whose benefits and costs count, and how much. This is typically referred to as determining standing. 2. Select the portfolio of alternative initiatives. 3. Catalog potential consequences and select measurement indicators. 4. Predict quantitative consequences over the life of the project for those who have standing. 5. Monetize (attach cash values to) all the predicted consequences. 6. Discount for time to find present values. 7. Sum up benefits and Costs for each initiative and Perform sensitivity analysis underlying key assumptions

Adapting to Climate Change 1

Adapting to Climate Change 2

Adapting to Climate Change 3

Adapting to Climate Change 4

Adapting to Climate Change 5 Source: Oregon Environmental Quality Commission, Oregon Climate Change Adaptation Framework. December 10, 2010, Salem OR