Simulation of Continuous Probability Distributions

Slides:



Advertisements
Similar presentations
Group Presentation for Kapahulu Restaurant Opening Kapahulu Restaurant Study by Chris Gilding David Main Rose-Marie Mercer.
Advertisements

Project Management Projects are unique, one-time operations designed to accomplish a specific set of objectives in a limited timeframe Project managers.
SE503 Advanced Project Management Dr. Ahmed Sameh, Ph.D. Professor, CS & IS Project Uncertainty Management.
Sampling: Final and Initial Sample Size Determination
Problem Activity Immediate Predecessor a m b A B 4 16
CHAPTER 4 MANAGING PROJECT PROCESSES. THE CONCEPT A project is an interrelated set of activities that has a definite starting and ending point and that.
1. Variance of Probability Distribution 2. Spread 3. Standard Deviation 4. Unbiased Estimate 5. Sample Variance and Standard Deviation 6. Alternative Definitions.
Assuming normally distributed data! Naïve Bayes Classifier.
ONE SAMPLE t-TEST FOR THE MEAN OF THE NORMAL DISTRIBUTION Let sample from N(μ, σ), μ and σ unknown, estimate σ using s. Let significance level =α. STEP.
More routing protocols Alec Woo June 18 th, 2002.
Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.
Representing Uncertainty CSE 473. © Daniel S. Weld 2 Many Techniques Developed Fuzzy Logic Certainty Factors Non-monotonic logic Probability Only one.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
CIS 410/510 Probabilistic Methods for Artificial Intelligence Instructor: Daniel Lowd.
SPS ITRT April Computation/ Estimation Number & Number Sense Measurement/ Geometry Probability/ Statistics Patterns, Functions, & Algebra
Prepared by: Michael Palazzo
Chapter 6 Time dependent reliability of components and system.
AP STATISTICS “Do Cell Phones Distract Drivers?”.
IRDM WS Source: Arnold O. Allen, Probability, Statistics, and Queueing Theory with Computer Science Applications, Academic Press, 1990 Reference.
EM and expected complete log-likelihood Mixture of Experts
Contagion in Networks Networked Life NETS 112 Fall 2013 Prof. Michael Kearns.
Chapter 10 Introduction to Simulation Modeling Monte Carlo Simulation.
Agresti/Franklin Statistics, 1e, 1 of 139  Section 6.4 How Likely Are the Possible Values of a Statistic? The Sampling Distribution.
computer
AP STATISTICS LESSON COMPARING TWO PROPORTIONS.
Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.
Scitor Tutorial 53:084 Project Design and Management in Civil Engineering Spring Semester 2003.
Chapter 7 PERT Project Management for Business, Engineering, and Technology.
Ch15: Decision Theory & Bayesian Inference 15.1: INTRO: We are back to some theoretical statistics: 1.Decision Theory –Make decisions in the presence of.
Change management control Sources of Change  Project scope changes  Implementation of contingency plans  Improvement changes ISE Ch. 6 1.
MATH 643 Bayesian Statistics. 2 Discrete Case n There are 3 suspects in a murder case –Based on available information, the police think the following.
FIN 614: Financial Management Larry Schrenk, Instructor.
CS 351/ IT 351 Modeling and Simulation Technologies Review ( ) Dr. Jim Holten.
Barcelona May 2003 BETA SESSION 4a: Distributed Generation Nikos HATZIARGYRIOU – Greece – BETA SESSION 4a: Distributed Generation Basic Elements.
Probability and Statistics Dr. Saeid Moloudzadeh Uniform Random Variable/ Normal Random Variable 1 Contents Descriptive Statistics Axioms.
Q1) A precedes C. B precedes D & E. C precedes F & G. D precedes G.
EXCERCISES ON BES. Compute the Break-even sales in pesos and units 1.A product line is sold at a unit selling price of P9.00. Variable cost is estimated.
Hypothesis Testing Steps for the Rejection Region Method State H 1 and State H 0 State the Test Statistic and its sampling distribution (normal or t) Determine.
1 Ardavan Asef-Vaziri Jan.-2016Basics Probability Distributions- Uniform.
Chapter 9 Sampling Distributions 9.1 Sampling Distributions.
Probabilistic Slope Stability Analysis with the
Statistics -Continuous probability distribution 2013/11/18.
Basic Probability Distributions
Variable cost (e.g. materials) = £5 per unit
Analytics and OR DP- summary.
Simulation Examples STOCHASTIC PROCESSES.
Project Management Simulation, U-Distribution
In-Class Exercise: The Poisson Distribution
Capacity Planning Simulation
TexPoint fonts used in EMF.
Representing Uncertainty
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Physics-based simulation for visual computing applications
Change in Expression after modifying fix_b?
أنماط الإدارة المدرسية وتفويض السلطة الدكتور أشرف الصايغ
Sampling Distribution
Sampling Distribution
Lecture Slides Elementary Statistics Twelfth Edition
Introduction to Probability & Statistics The Central Limit Theorem
Lecture Slides Elementary Statistics Twelfth Edition
Change in Expression after modifying fix_b?
Change in Expression after modifying fix_b?
Change in Expression after modifying fix_b?
From Simulations to the Central Limit Theorem
Additional notes on random variables
Additional notes on random variables
Project Management CPM/PERT Professor Ahmadi.
Exact Test Fisher’s Statistics
Example Exercise 5 Sales Mix and Break-Even Analysis
Chapter 6 Time dependent reliability of components and system
Presentation transcript:

Simulation of Continuous Probability Distributions

Simulation of Break-Even Analysis Fixed Cost =INT(A$3+(A$4-A$3)*RAND()) Variable Cost=INT(B$3+(B$4-B$3)*RAND()) Sales Price=INT($C$3+$C$4*NORM.S.INV(RAND())) Sales =-INT($D$3*LN(RAND()))

Simulation of Break-Even Analysis

Simulation Simulation helps us to overcome our shortcomings in analysis of complex systems using statistics, and also to see the dynamics of the system. Statistics vs. Simulation: To compute probability of completion time or cost of a network of activities. Both must enumerate all the paths to compute the probability Statistics assume path interdependence while simulation does not For Simplicity, Triangular distribution is used to estimate Beta distribution.