《工程测试与信号处理》课程授课教案(课件讲稿)Part 3 Measurement Errors & Uncertainity Analysis

Part 3: Measurement Errors &Uncertainity Analysis
Part 3: Measurement Errors & Uncertainity Analysis

Lecture 5Probability and Statistics
Lecture 5 Probability and Statistics

LearningObjectives()quantify the statistical characteristics of a data set as itrelates to the population of the measured variable.explain and use probability density functions to describethe behaviour of variables,create meaningful histograms of measured data,quantify a confidence interval about the measured meanvalue at a given probability
Learning Objectives (1) • quantify the statistical characteristics of a data set as it relates to the population of the measured variable, • explain and use probability density functions to describe the behaviour of variables, • create meaningful histograms of measured data, • quantify a confidence interval about the measured mean value at a given probability

LearningObjectives(2)perform regression analysis on a data set and quantifythe confidence intervals for theparameters of the resulting curve fit or response surface.identify outliers in a data set,specify the number of measurements required to achievea desired confidence interval, andexecute a Monte Carlo simulation that predicts thebehaviour expected in a result due tovariations in the variables involved in computing thatresult
Learning Objectives (2) • perform regression analysis on a data set and quantify the confidence intervals for the • parameters of the resulting curve fit or response surface, • identify outliers in a data set, • specify the number of measurements required to achieve a desired confidence interval, and • execute a Monte Carlo simulation that predicts the behaviour expected in a result due to • variations in the variables involved in computing that result

STATISTICALMEASUREMENTTHEORY Sampling refers to obtaining a set of data throughrepeated measurements of a variable under fixedoperating conditions. This variable is known asthe measured variable or, in statistical terms, themeasurand
STATISTICAL MEASUREMENT THEORY • Sampling refers to obtaining a set of data through repeated measurements of a variable under fixed operating conditions. This variable is known as the measured variable or, in statistical terms, the measurand

AssumptionsStudying the effects of random errors and how to quantifythem. Random errors are manifestedthrough data scatter and by the statistical limitations of a finite sampling topredict the behaviour of a population.We assume that any systematic errors in the measurementare negligible (average error in the data set is zero)
Assumptions Studying the effects of random errors and how to quantify them. Random errors are manifested • through data scatter and • by the statistical limitations of a finite sampling to predict the behaviour of a population. We assume that any systematic errors in the measurement are negligible (average error in the data set is zero)

SettingtheproblemEstimate the true value, xo, based on the informationderived from the repeated measurement of variable xIn the absence of systematic error, the true value of x is themean value of all possible values of x
Setting the problem Estimate the true value, x0, based on the information derived from the repeated measurement of variable x. In the absence of systematic error, the true value of x is the mean value of all possible values of x

Estimationoftruevalue From a statistical analysis of the data set and an analysisof sources of error that influence these data, we canestimate x'asx'=x+u(P%)x - most probable estimate of xUx - uncertainty intervalP% - probability level
Estimation of true value • From a statistical analysis of the data set and an analysis of sources of error that influence these data, we can estimate x’ as x x u (P%) x x - most probable estimate of x’ P% ux - uncertainty interval - probability level

ProbabilityDensityFunctions(1)During repeated measurements of a variable, each datapoint may tend to assume one preferred value or liewithin some interval about this value more often thannot, when all the data are compared.This tendency toward one central value about which allthe other values are scattered is known as a centraltendency of a random variable.The central value and those values scattered about it canbe determined from the probability density of themeasured variable
Probability Density Functions (1) • During repeated measurements of a variable, each data point may tend to assume one preferred value or lie within some interval about this value more often than not, when all the data are compared. • This tendency toward one central value about which all the other values are scattered is known as a central tendency of a random variable. • The central value and those values scattered about it can be determined from the probability density of the measured variable

ProbabilityDensityFunctions(2) The frequency with which the measured variableassumes a particular value or interval of values isdescribed by its probability density
Probability Density Functions (2) • The frequency with which the measured variable assumes a particular value or interval of values is described by its probability density
按次数下载不扣除下载券;
注册用户24小时内重复下载只扣除一次;
顺序:VIP每日次数-->可用次数-->下载券;
- 《工程测试与信号处理》课程授课教案(课件讲稿)Part 2 Measurement System Behaviour.pdf
- 《数字通信》课程教学课件(讲稿)SDN Fundamentals & Techniques.pdf
- 《数字通信》课程教学课件(讲稿)Mobile Communication Systems Part I.pdf
- 《数字通信》课程教学课件(讲稿)Mobile Communication Systems Part II 5G NW Architecture, 4G/5G Co-operation Models & NW Selection.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)通信系统原理总复习.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第3章 模拟调制系统(复习与习题).pdf
- 武汉理工大学:《通信原理》课程实验指导书(共六个实验).pdf
- 武汉理工大学:《通信原理》课程实验教学大纲 Communication Principles A.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第8章 纠错编码技术.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第7章 现代调制技术.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第6章 基本的数字调制技术.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第5章 模拟信号数字化.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第4章 数字信号的基带传输.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第3章 模拟调制系统.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第2章 通信信号分析.pdf
- 武汉理工大学:《通信原理》课程教学资源(课件讲稿)第1章 通信系统概述.pdf
- 《通信原理》课程教学资源(文献资料)中华人民共和国无线电频率划分规定(2010年12月1日起施行).pdf
- 武汉理工大学:《通信原理》课程教学大纲 Communication Principles A.pdf
- 《EDA技术》课程教学资源(课件讲稿)第6章 QUARTUSΙΙ宏功能模块应用.pdf
- 《EDA技术》课程教学资源(课件讲稿)第7章 VHDL语句.pdf
- 《工程测试与信号处理》课程授课教案(课件讲稿)Part 1 Principles of Measurement Methods Mihai Nicolescu.pdf
- 《工程测试与信号处理》课程教学资源(作业习题)Measurement systems and SignalProcessing(含参考答案).pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Strain Measurement.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Pressure and Velocity.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)TEMPERATURE MEASUREMENT.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Sensors_proximity.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Presentation of Material.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Presentation.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)FORCE MEASUREMENTS.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)FORCE and Torque.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Flow Measurement.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Displacement and Area.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Actuators.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Actuators_elecrical.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)ACTUATORS2.pdf
- 《工程测试与信号处理》课程教学资源(文献资料)Abrasive Machining and Finishing Operations(PPT).ppt
- 《工程测试与信号处理》课程教学资源(文献资料)Abrasive Waterjet Machining(PPT).ppt
- 《数字通信》课程教学大纲 Digital Communication(研究生).doc
- 《工程测试与信号处理》课程教学大纲 Engineering Test and Data Processing(工程测试与数据处理).doc
- 《工程测试与信号处理》课程教学资源(实验指导)环形传送带工件计数综合实验.doc