ETM 607 – Putting It All Together Review for MidTerm II Apply Lessons Learned in a Team Lab - Input Modeling - Absolute Output Analysis - Relative Output.

Slides:

Advertisements

Similar presentations

Hypothesis Testing I 2/8/12 More on bootstrapping Random chance

Advertisements

Chapter 8 Random-Variate Generation

Chapter 8 Random-Variate Generation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation.

 1  Outline  Model  problem statement  detailed ARENA model  model technique  Output Analysis.

Chapter 8 Interval Estimation Population Mean:  Known Population Mean:  Known Population Mean:  Unknown Population Mean:  Unknown n Determining the.

Simulation Where real stuff starts. ToC 1.What, transience, stationarity 2.How, discrete event, recurrence 3.Accuracy of output 4.Monte Carlo 5.Random.

Simulation Modeling and Analysis

Additional Topics in Regression Analysis

Building and Running a FTIM n 1. Define the system of interest. Identify the DVs, IRVs, DRVs, and Objective. n 2. Develop an objective function of these.

Agenda Purpose Prerequisite Inverse-transform technique

Variance Reduction Techniques

Analysis of Simulation Input.. Simulation Machine n Simulation can be considered as an Engine with input and output as follows: Simulation Engine Input.

Simulation Input and Output Data Analysis

1 Simulation Modeling and Analysis Pseudo-Random Numbers.

1 Simulation Modeling and Analysis Output Analysis.

Some standard univariate probability distributions

Monté Carlo Simulation MGS 3100 – Chapter 9. Simulation Defined A computer-based model used to run experiments on a real system.  Typically done on a.

ETM 607 – Random Number and Random Variates

Correlation and Linear Regression

Hypothesis Testing in Linear Regression Analysis

Chapter 5 Modeling & Analyzing Inputs

1 Validation & Verification Chapter VALIDATION & VERIFICATION Very Difficult Very Important Conceptually distinct, but performed simultaneously.

© 2002 Prentice-Hall, Inc.Chap 14-1 Introduction to Multiple Regression Model.

Steady-State Statistical Analysis By Dr. Jason Merrick.

Input Analysis 1.  Initial steps of the simulation study have been completed.  Through a verbal description and/or flow chart of the system operation.

Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.

Modeling and Simulation CS 313

2 Input models provide the driving force for a simulation model. The quality of the output is no better than the quality of inputs. We will discuss the.

1 Statistical Distribution Fitting Dr. Jason Merrick.

© 2003, Carla Ellis Simulation Techniques Overview Simulation environments emulation exec- driven sim trace- driven sim stochastic sim Workload parameters.

Chapter 7 Random-Number Generation

IE 429, Parisay, January 2010 What you need to know from Probability and Statistics: Experiment outcome: constant, random variable Random variable: discrete,

ETM 607 – Input Modeling General Idea of Input Modeling Data Collection Identifying Distributions Parameter estimation Goodness of Fit tests Selecting.

CHAPTER 11 SECTION 2 Inference for Relationships.

Goodness-of-Fit Chi-Square Test: 1- Select intervals, k=number of intervals 2- Count number of observations in each interval O i 3- Guess the fitted distribution.

Fitting probability models to frequency data. Review - proportions Data: discrete nominal variable with two states (“success” and “failure”) You can do.

Selecting Input Probability Distribution. Simulation Machine Simulation can be considered as an Engine with input and output as follows: Simulation Engine.

Example: Bioassay experiment Problem statement –Observations: At each level of dose, 5 animals are tested, and number of death are observed.

Probability = Relative Frequency. Typical Distribution for a Discrete Variable.

A BETTER ALLOCATION TO REDUCE VOTING QUEUE LENGTH CMP606 – Group777 Enas Mohamed Hisham Naiem Mostafa Izz Department of Computer Engineering Faculty of.

1 OUTPUT ANALYSIS FOR SIMULATIONS. 2 Introduction Analysis of One System Terminating vs. Steady-State Simulations Analysis of Terminating Simulations.

ETM 607 – Output Analysis: Estimation of Relative Performance Output comparison between two or more alternative systems Common Random Numbers (CRN) Comparison.

IE 300, Fall 2012 Richard Sowers IESE. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples.

Chapter 9 Input Modeling

2 Input models provide the driving force for a simulation model. The quality of the output is no better than the quality of inputs. In this chapter, we.

Chapter 3: Uncertainty "variation arises in data generated by a model" "how to transform knowledge of this variation into statements about the uncertainty.

Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.

MONTE CARLO METHOD DISCRETE SIMULATION RANDOM NUMBER GENERATION Chapter 3 : Random Number Generation.

Statistics and probability Dr. Khaled Ismael Almghari Phone No:

Modeling and Simulation CS 313

I. ANOVA revisited & reviewed

Lecture Slides Elementary Statistics Twelfth Edition

Chapter 4: Sampling and Statistical Inference

Chapter 4. Inference about Process Quality

Modeling and Simulation CS 313

Basic MC/Defn/Short Answer/Application Cumulative

Elementary Statistics

Inferences On Two Samples

Goodness of Fit Tests The goal of χ2 goodness of fit tests is to test is the data comes from a certain distribution. There are various situations to which.

Since everything is a reflection of our minds,

Statistical Methods For Engineers

Goodness-of-Fit Tests

Survival Analysis {Chapter 12}

Discrete Event Simulation - 4

Chapter 8 Interval Estimation

Chapter 8 Random-Variate Generation

Section 3: Estimating p in a binomial distribution

Estimating the Value of a Parameter Using Confidence Intervals

Chapter 8 Supplement Forecasting.

Where real stuff starts

Presentation transcript:

ETM 607 – Putting It All Together Review for MidTerm II Apply Lessons Learned in a Team Lab - Input Modeling - Absolute Output Analysis - Relative Output Analysis

ETM 607 – MidTerm Review o Chapter 7 – Random Number Generation - Generation Methods Linear Congruential Method - Tests Frequency (Chi Squared and Kolmogorov-Smirnov) Autocorrelation o Chapter 8 – Random Variate Generation - Inverse transform method Discrete distributions Continuous distributions

ETM 607 – MidTerm Review o Chapter 9 – Input Modeling - Histograms and Selecting Distribution Families - Parameter Estimation - Goodness of Fit Tests Chi Squared Kolmogorov-Smirnov) Fitting Non-Stationary Poisson Process o Chapter 10 – Estimation of Absolute Performance - Point estimation - Confidence Intervals - Initialization Bias - Determining number of Replications (desired CI width)

ETM 607 – MidTerm Review o Chapter 12 – Estimation of Relative Performance - Comparison of Two systems Independent sampling CRN approach CI overlap or containing the value 0 Logistics Monday 6-9pm, December 3 Open book and notes No computer section Computer section - Excel

ETM 607 – Putting It All Together Base Entrance with Guard Booths 2 guards in series

ETM 607 – Putting It All Together Simulation Study: Guard Booth Analysis Assume you have collected vehicle counts at 15 minute intervals and the times for guards to process vehicles. This data has been tabulated in an Excel spreadsheet “Lesson13.xls”. A lane can either be manned by one or two guards. Times to process vehicles are slightly longer in the two guard case to allow for vehicle to advance to second guard.

ETM 607 – Putting It All Together Simulation Study: Guard Booth Analysis Objective: Determine the best staffing levels throughout the day. Alternatives to evaluate – how many lanes / guards, and if and when to use a one guard or two guard lane option. An Arena model with logic has been provided. As a team, evaluate alternatives and provide a recommendation. Back up your recommendation with data.

ETM 607 – Putting It All Together Simulation Study: Guard Booth Analysis Keep In Mind: Do you need to model every 15 minute interval? How are you to develop your input models? What is your design of experiment (i.e. scenarios) How are you going to compare scenarios? What performance measures do you believe to be important? How wide do you want your confidence intervals? How will you present your recommendations?