This document presents information on sensitivity analysis techniques. It discusses how sensitivity analysis is used to determine how changes to independent variables impact dependent variables given certain assumptions. It also describes how sensitivity analysis can predict outcomes if a situation differs from key predictions. Various sensitivity analysis methods are outlined, including correlation and screening techniques, regression analysis, and analyzing oscillations through measuring behavior patterns like period and amplitude. An example of applying sensitivity analysis to a simple supply chain model is also provided.
-What is Sensitivity Analysis in Project Risk Management?
-Example on Sensitivity Analysis….
-Types of Sensitivity Analysis……
-Advantages & Disadvantages
What Does Sensitivity Analysis Mean?
A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in interest rates will have on a bond's price.
Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s).
-What is Sensitivity Analysis in Project Risk Management?
-Example on Sensitivity Analysis….
-Types of Sensitivity Analysis……
-Advantages & Disadvantages
What Does Sensitivity Analysis Mean?
A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in interest rates will have on a bond's price.
Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s).
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
This presentation is made to represent the basic transportation model. The aim of this presentation is to implement the transportation model in solving transportation problem.
Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables.
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
This presentation is made to represent the basic transportation model. The aim of this presentation is to implement the transportation model in solving transportation problem.
Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables.
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
This is the basic knowledge of an IT Infrastructer.Which help to understand the requirment of IT ,Clients ,server and the work flow of the Information. The network connection and the Layars of OSI model to understand how the data is moving in a network.
This 2-hour course will show the perspective of what it takes to be an effective CFO in the construction industry from the vantage point of CFO’s, bankers, surety agents and other professional references who work in the industry. This session will examine best practices and protocols of construction financial management.
Learning Objectives/ Outcomes:
• Review and continuation of construction financial statements; using a work-in-progress report
• Review and continuation of job costing and accounting
• “Cash is King” rule of thumb summary
• Discuss the role of owner’s right hand person
Risk and uncertainty are related, but different concepts that many people struggle to understand. This presentation defines and explains the difference between risk and uncertainty and how they are measured, so that they can be properly managed in a business context.
Please add any comments or feedback, and share this presentaiton with your colleagues, thanks!
Feel free to contact me via LinkedIn if you have any questions:
http://www.linkedin.com/in/kelvinstott
Alternatively, please visit or join our LinkedIn group, ’Big Ideas in R&D Productivity & Project / Portfolio Management’:
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Recently, Construction IQ conducted an online survey on construction project risk management. Some valuable statistics emerged from the results. Have a look at what your colleagues and peers in the industry had to say…
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
On Confidence Intervals Construction for Measurement System Capability Indica...IRJESJOURNAL
Abstract: There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification. For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi. In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelinesin the use of the GCI approach. We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
Data science is likely to become even more important as the volume and complexity of data continues to increase. With advancements in machine learning and artificial intelligence, data scientists will have access to more sophisticated tools and algorithms to analyze and extract insights from data. Data science will continue to play a crucial role in fields such as healthcare, finance, and technology, helping organizations make better decisions and drive innovation. Additionally, there will be a greater emphasis on data privacy and ethical considerations as the use of data becomes more prevalent.
#Data science is a field that involves using statistical and computational methods to analyze and extract insights from data. It plays a crucial role in various industries, from business and healthcare to finance and technology.
Sensitivity Analysis, Optimal Design, Population Modeling.pptxAditiChauhan701637
Sensitivity analysis is the study of the unreliability related to output and input of mathematical model or numerical system which can be divided and allocated to various sources.
The process of outcome under possible speculation to find out the impact of a variable under sensitivity analysis can be useful for a range of purpose, consisting -
1. In the existence of unreliability, prefer testing of the results of a model or system.
2. Enhanced understanding of correlation between input and output variables in a model or system.
Sensitivity analysis methods:
There are many number of methods to study the sensitivity analysis, many of which have been developed to address one or more of the limitations discussed above. By the type sensitivity analysis measurement they are differentiate, be it based on variance decompositions, partial derivatives or elementary effects.
Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables.
ON THE PREDICTION ACCURACIES OF THREE MOST KNOWN REGULARIZERS : RIDGE REGRESS...ijaia
The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the
predication accuracy depends heavily on the nature of the sampled datasets.
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Classification of mathematical modeling,
Classification based on Variation of Independent Variables,
Static Model,
Dynamic Model,
Rigid or Deterministic Models,
Stochastic or Probabilistic Models,
Comparison Between Rigid and Stochastic Models
- The physical-mathematical model of the actual natural or technological phenomena can include
different variables, the finite amount of which is defined by a researcher/conscious observer. The a priori
overall error inherent this model due its finiteness could be compared with the actual experimental measurement
error and should be useful in guiding future investigations. In this context, we propose a strategy relying on
thermodynamic theory of information processes, to estimate this error that cannot be done an arbitrarily small.
For the considered assumptions, the calculated error of the main researched variable, measured in conventional
field studies, should not be less than the error caused by the limited number of dimensional variables of the
physical-mathematical model. Examples of practical application of the proposed concept for spacecraft heating,
climate prediction, thermal energy storage and food freezing are discussed
2. A technique used to determine how different values of an
independent variable will impact a particular dependent variable
under a given set of assumptions. This technique is used within
specific boundaries that will depend on one or more input variables,
such as the effect that changes in interest rates will have on a
bond's price.
Sensitivity analysis is a way to predict the outcome of a decision if
a situation turns out to be different compared to the key
prediction(s).
2
3. Create A Model
Write A Set Of Requirements
Design A System
Make A Decision
Do A Tradeoff Study
Originate A Risk Analysis
Want To Discover The Cost Drivers
3
4. Uncertainty in various parameters used in Simulation Models –
Feedback loops, Probability Distributions etc.
Values of these parameters cannot be estimated precisely due to
data availability or time constraints.
Less Reliable Models: tested for their sensitivity to the changes in
model components.
Components, to which simulation results are sensitive, need more
attention than other parts of the model.
Parameter sensitivity of the model can be compared with the
information from real system.
4
5. The genetics studies on the pea by Gregor Mendel, 1865.
The statistics studies on the Irish hops crops by Gosset (reported
under the pseudonym Student), ca 1890.
5
6. The problematic behavior is tried to be explained by system
structure.
The behavior pattern of the system is the main interest of analysts
rather than the specific values of the variables.
Therefore, a behavior-pattern-oriented approach should be applied
to sensitivity analysis
It is difficult to analyze oscillations with correlation based statistical
methods using the values of model variables, hence this approach is
more significant
6
7. System oscillation is the characteristic symptom of negative
feedback structures in which the information used to take goal-
seeking action is delayed
7
9. In the study conducted by Ford (1990), sensitivity of results are measured
by partial correlation coefficients which indicate the strength of linear
relationship between two variables after the effects of other variables are
removed
Ford and Flynn (2005) propose Pearson correlation coefficients instead of
the partial ones for simpler sensitivity analysis procedure. This method is
named as screening
Sterman (2000) proposes that numerical sensitivity can be used for
simulation models which work with great numerical precision such as
models in physics or light simulators.
Policy sensitivity is designed as the changes in the optimal policy when
parameters values change (Moxnes, 2005; Sterman, 2000).
9
10. Behavior measures are the subject matter of system dynamics studies in
which the problematic behavior is analyzed with respect to its structure.
For instance a population in an isolated area, which follows a boom and
bust behavior, can be explained by depleting resources and vanishing birth
rate.
Sensitivity of the behavior to the model parameters can be analyzed by
using peak or equilibrium level of the behavior pattern.
Behavior pattern sensitivity aims to explore the effect of varying model
inputs on the specific behavior measures.
10
12. Correlation & Screening Method
Ford and Flynn (2005) suggest Pearson correlation in order to conduct quick
sensitivity analysis, called screening.
In this method, correlation coefficients between the output and each parameter
are calculated and plotted against simulation time
Parameters that have high correlation with output variable are concluded to be
the high sensitivity ones
Regression Method
Another convenient method assuming linear relationship between variables is
regression.
In this method, an equation that minimizes the sum of squares of residual terms
is calculated by using ordinary least squares algorithm (Draper and Smith, 1998).
When a nonlinear relationship is detected at the diagnosis of regression model,
one can utilize transformation on either dependent or independent variables. 12
13. Statistical Analysis (ANOVA) of Clusters in Scatter Plots
Another efficient way of dealing with nonlinearity between system output and
parameters is using statistical analysis of clusters in which output variable y is
plotted against each parameter xj and this plot is subject to statistical analysis
after it is divided into several clusters (Kleijnen and Helton, 1999a).
This method is a more general way of one-variate sensitivity analysis since it
does not have the linearity assumption between dependent and independent
variables, i.e. this is a statistical model independent method" (Saltelli et al.,
2000).
The scatter plot for each parameter is subject to statistical analysis in order to
detect any non-random pattern.
13
14. Figure: Stock Flow Structure of Simple Supply Line Model by Sterman (2000)
14
15. Supply chains are good examples of material delay formulations that are
rigorously discussed in system dynamics literature.
Supply chains consist of a stock and flow structure for the acquisition,
storage and conversion of inputs into outputs and the decision rules
governing these flows
Include negative feedback loops that create corrective action once
discrepancy arises between the stock and its desired level
The transformation process in each supply chain takes some amount of
time, i.e. there is a time delay in every supply chain structure.
Interaction between negative feedback loops and the time lag may yield
oscillations
15
16. OSCILLATION
Oscillations are cyclic behavior patterns which are difficult to analyze with
standard statistical techniques, such as screening.
So, sensitivity analysis of oscillatory models should focus on the pattern measures
of these behavior modes
Common measures of an oscillatory pattern are period, first amplitude and
amplitude slope.
Period of an oscillation is amount of time between two successive peaks or
troughs.
This pattern measure indicates how much the system oscillates under certain
circumstances.
One of the critical steps in pattern sensitivity analysis procedure is the estimation
of pattern measures for each simulation run.
16
17. In this thesis, periods are estimated by autocorrelation function in BTSII which is
a validation tool for behavior pattern tests of system dynamics models.
According to the results of regression analysis, stock adjustment time is the most
important parameter of the model.
The second important parameter is acquisition lag which is the average time lag
in supply line. Increasing values of this parameter yields longer-period oscillatory
systems.
17
20. A methodology for statistical sensitivity analysis of system dynamics models by
Mustafa Hekimoglu
(www.Ie.Boun.Edu.Tr/labs/sesdyn/publications/theses/ms_hekimoglu.Pdf)
20
Editor's Notes
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