1 Quantifying & Propagation of Uncertainty Module 2 Lecture THREE (4-4) 3/14/05.

Slides:



Advertisements
Similar presentations
Modeling of Data. Basic Bayes theorem Bayes theorem relates the conditional probabilities of two events A, and B: A might be a hypothesis and B might.
Advertisements

Modelling unknown errors as random variables Thomas Svensson, SP Technical Research Institute of Sweden, a statistician working with Chalmers and FCC in.
ECE 8443 – Pattern Recognition LECTURE 05: MAXIMUM LIKELIHOOD ESTIMATION Objectives: Discrete Features Maximum Likelihood Resources: D.H.S: Chapter 3 (Part.
General Statistics Ch En 475 Unit Operations. Quantifying variables (i.e. answering a question with a number) 1. Directly measure the variable. - referred.
Slide 1-1 Copyright © 2004 Pearson Education, Inc. Stats Starts Here Statistics gets a bad rap, and Statistics courses are not necessarily chosen as fun.
Experimental Uncertainties: A Practical Guide What you should already know well What you need to know, and use, in this lab More details available in handout.
EVAL 6970: Meta-Analysis Heterogeneity and Prediction Intervals Dr. Chris L. S. Coryn Spring 2011.
EART20170 Computing, Data Analysis & Communication skills
Types of Errors Difference between measured result and true value. u Illegitimate errors u Blunders resulting from mistakes in procedure. You must be careful.
Example: Cows Milk Benefits –Strong Bones –Strong Muscles –Calcium Uptake –Vitamin D Have you ever seen any statistics on cow’s milk? What evidence do.
Statistics and Probability Theory Prof. Dr. Michael Havbro Faber
Week Lecture 3Slide #1 Minimizing e 2 : Deriving OLS Estimators The problem Deriving b 0 Deriving b 1 Interpreting b 0 and b 1.
Generation & Propagation of Uncertainty Analysis P M V Subbarao Professor Mechanical Engineering Department A Measure of Confidence Level in compound Experiments…..
1 Seventh Lecture Error Analysis Instrumentation and Product Testing.
FIN357 Li1 The Simple Regression Model y =  0 +  1 x + u.
Differential Equations Dillon & Fadyn Spring 2000.
Basics of Sampling Theory P = { x 1, x 2, ……, x N } where P = population x 1, x 2, ……, x N are real numbers Assuming x is a random variable; Mean/Average.
Standard error of estimate & Confidence interval.
Differentials, Estimating Change Section 4.5b. Recall that we sometimes use the notation dy/dx to represent the derivative of y with respect to x  this.
Partial and Total derivatives Derivative of a function of several variables Notation and procedure.
MAT 1236 Calculus III Section 14.5 The Chain Rule
Introduction to Error Analysis
An Introduction to the Statistics of Uncertainty
More U-Substitution February 17, Substitution Rule for Indefinite Integrals If u = g(x) is a differentiable function whose range is an interval.
Combined Uncertainty P M V Subbarao Professor Mechanical Engineering Department A Model for Propagation of Uncertainty ….
Ch 8 Estimating with Confidence. Today’s Objectives ✓ I can interpret a confidence level. ✓ I can interpret a confidence interval in context. ✓ I can.
Scientific Measurement Making Sensible Measurements.
MECN 3500 Inter - Bayamon Lecture 9 Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
1 Review from previous class  Error VS Uncertainty  Definitions of Measurement Errors  Measurement Statement as An Interval Estimate  How to find bias.
Mathematics. Session Differential Equations - 2 Session Objectives  Method of Solution: Separation of Variables  Differential Equation of first Order.
Matrix Differential Calculus By Dr. Md. Nurul Haque Mollah, Professor, Dept. of Statistics, University of Rajshahi, Bangladesh Dr. M. N. H. MOLLAH.
Inference for 2 Proportions Mean and Standard Deviation.
Is statistics relevant to you personally? Bush Dukakis Undecided Month 1 Month 2 Headline: Dukakis surges past Bush in polls!  4% 42% 40% 18% 41% 43%
Confidence intervals. Estimation and uncertainty Theoretical distributions require input parameters. For example, the weight of male students in NUS follows.
Repeated Measures Analysis of Variance Analysis of Variance (ANOVA) is used to compare more than 2 treatment means. Repeated measures is analogous to.
INTRODUCTORY LECTURE 3 Lecture 3: Analysis of Lab Work Electricity and Measurement (E&M)BPM – 15PHF110.
1 Sampling Distribution of Arithmetic Mean Dr. T. T. Kachwala.
CHAPTER 5 PARTIAL DERIVATIVES
Math Review and Lessons in Calculus
Analysis of Experimental Data; Introduction
Chapter 22 Comparing Two Proportions. Comparing 2 Proportions How do the two groups differ? Did a treatment work better than the placebo control? Are.
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 7: Regression.
Chapter 8 Estimation ©. Estimator and Estimate estimator estimate An estimator of a population parameter is a random variable that depends on the sample.
Section 13.3 Partial Derivatives. To find you consider y constant and differentiate with respect to x. Similarly, to find you hold x constant and differentiate.
Learning Theory Reza Shadmehr Distribution of the ML estimates of model parameters Signal dependent noise models.
Engineering 1181 College of Engineering Engineering Education Innovation Center Collecting Measured Data Classroom Lecture Slides.
Business Calculus Derivative Definition. 1.4 The Derivative The mathematical name of the formula is the derivative of f with respect to x. This is the.
CIVE Engineering Mathematics 1.1 Length of a curve Double Integrals - for calculating areas - for calculating volumes (Estimating errors using the.
DYNAMIC BEHAVIOR OF PROCESSES :
Chi Square Test of Homogeneity. Are the different types of M&M’s distributed the same across the different colors? PlainPeanutPeanut Butter Crispy Brown7447.
MECH 373 Instrumentation and Measurements
Chapter 1: Definitions, Families of Curves
Section 14.2 Computing Partial Derivatives Algebraically
Antiderivatives 5.1.
Chain Rules for Functions of Several Variables
Business Mathematics MTH-367
FIRST ORDER DIFFERENTIAL EQUATIONS
CHAPTER 5 PARTIAL DERIVATIVES
ECONOMETRICS DR. DEEPTI.
Physics 114: Lecture 10 Error Analysis/ Propagation of Errors
الأستاذ المساعد بقسم المناهج وطرق التدريس
Find the first partial derivatives of the function. {image}
ECE 417 Lecture 4: Multivariate Gaussians
Measurement Uncertainty Analysis
Integration.
Sampling and Sample Size Calculations
Homoscedasticity/ Heteroscedasticity In Brief
Probability, Statistics
Homoscedasticity/ Heteroscedasticity In Brief
Comparing Theory and Measurement
Presentation transcript:

1 Quantifying & Propagation of Uncertainty Module 2 Lecture THREE (4-4) 3/14/05

2 What have you learned so far? Determine Random Uncertainty in the Measurement of the Measurand Using Single measurement Using ONE sample Using M samples Determine Overall Random uncertainty caused by Elemental Errors Determine Total Uncertainty caused by Bias and Random uncertainties Determine Total Uncertainty caused by more than ONE variable

3 Random & Bias Errors Single Measurement B x =0

4 Random & Bias Errors in Multiple Measurements

5 Determination of Total (Systematic and Random ) Uncertainties Total Systematic and Random Uncertainty W x (RSS) B i ’s are the systematic uncertainties caused by k elemental error sources and Pi’s are the random uncertainties caused by m elemental error sources

6 Examples Lecture Slides (check values?) Readings (course web)

7 Mathematical Approach for Determining Uncertainties o It allows us to study the impact of uncertainties caused by MORE THAN ONE INDEPENDENT variable on the TOTAL uncertainty on the DEPENDENT variable o The mathematical mechanism to do this is “Partial Derivative”.

8 Calculate Total uncertainty dR is the Total uncertainty in the measurement of R (result-dependent variable) caused by Elemental uncertainties dx i in the variables x i (independent variables) Using the RSS method

9 Partial Derivative - Notation The “total” change in the area is represented by the derivative dA as Total Change in area Partial Change in area due to d W Partial Change in area due to d L

10 In Terms of Uncertainty in Measurements Total Uncertainty Sensitivity of A with respect to L Uncertainty in L Uncertainty in W Sensitivity of A with respect to W

11 TOTAL Uncertainty of Dependent Variable in Terms of Random and Bias Uncertainties of Independent Variables Uncertainty in L Uncertainty in W

12 Determine Total Uncertainty Using the RSS Formula Assume the dimensions of the rectangular (LxW= 60x50) and uncertainties in the measurements of L and W are mm and mm, respectively. Meaning? We are 95% Confident that True Value of Area = m 2 Assuming that THERE is No Bias Errors Other wise We are 95% Confident that Mean Value of Area Measurements (population) = m 2

13 How to perform PD? Transform the Multi-variable function to ONE variable function replace all variables with constants, except the ONE variable that is differentiated Perform ordinary differentiation Replace back the constants with the equivalent variables

14 Example: Area A = L x W

15 Example: Area A = L x W

16 Example: Area A = L x W

17 Example: Resistance

Another Differentiation Rule?

21 Example: Resistance

22 Example: Resistance Determine the Total uncertainty in measuring the resistance R, for the nominal values of L = 2m, A= 1 mm 2, and resistivity = 0.025x10 -6 Ω.m. The uncertainties in the measurement of L, A, and resistivity are m, + 0.1mm 2, and x10 -6 Ω.m, respectively?

23 Example: Electrical Resistance General Formula

24 Example: Electrical Resistance The RSS formula

26 Determine Total Fractional Uncertainty Using Fractional Uncertainties of Variables If the dependent variable R is a product of the measured variables, i.e Then, the fractional uncertainty in R is directly related to the fractional uncertainty of the variables

27 The Two Forms are Equivalent Which is equivalent to

28 Back to Area Example Formula for Area A = L x W

31 Any Question????? Any Question???? Danke Schon Thank you Good Luck with Exam