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Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
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or
call us at : 098153-33456
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Parametric estimation of construction cost using combined bootstrap and regre...IAEME Publication
The document discusses a method for estimating construction costs using a combined bootstrap and regression technique. It involves using historical project data to develop a regression model relating cost to key parameters. A bootstrap resampling method is then used to generate multiple simulated datasets from the original. Regression analysis is performed on each resampled dataset to calculate coefficients and develop a cost range estimate that captures uncertainty. This allows integrating probabilistic and parametric estimation methods while requiring fewer assumptions than traditional statistical techniques. The goal is to provide more accurate conceptual cost estimates early in projects when design information is limited.
Quantitative Risk Assessment - Road Development PerspectiveSUBIR KUMAR PODDER
This document outlines an approach for quantitative risk assessment in road transport infrastructure projects using stochastic analysis with triangular distributions. It discusses determining the combined influence of parameters like project cost and traffic on economic indicators. Traditionally, risks from cost and traffic changing from base cases are analyzed separately using triangular distributions defined by minimum, most likely and maximum limits. The document proposes a method to analyze the combined influence of both parameters varying simultaneously using bivariate distributions and conditional probabilities.
This document provides an overview of experimental design, response surface analysis, and optimization. It discusses key terminology like factors, responses, and treatments. Full factorial designs are explained as a way to systematically vary factor levels to estimate main and interaction effects. Response surfaces and metamodels like regression analysis are introduced as tools to approximate simulation responses. Response surface methodology is outlined as a sequential approach combining metamodeling and optimization. Examples help illustrate concepts like estimating effects from a full factorial design. Situations with many factors and terminology in experimental design are also covered at a high level.
This document provides an overview of demand estimation and regression analysis. It discusses how demand estimation is an essential process that informs various business decisions. Regression analysis uses statistical techniques to model the relationship between a dependent variable (e.g. demand) and independent variables (e.g. price, income). Simple regression uses one independent variable, while multiple regression uses more variables. Ordinary least squares is used to estimate the coefficients in the regression equation. These coefficients represent the impact of each independent variable on demand and can be used to forecast demand under different scenarios.
The Sample Average Approximation Method for Stochastic Programs with Integer ...SSA KPI
The document describes a sample average approximation method for solving stochastic programs with integer recourse. It approximates the expected recourse cost function using a sample average based on a sample of scenarios. It shows that as the sample size increases, the solution to the sample average approximation problem converges exponentially fast to the optimal solution of the true stochastic program. It also describes statistical and deterministic techniques for validating candidate solutions. Preliminary computational results applying this method are also mentioned.
Adaptive response surface by kriging using pilot points for structural reliab...IOSR Journals
Structural reliability analysis aims to compute the probability of failure by considering system uncertainties. However, this approach may require very time-consuming computation and becomes impracticable for complex structures especially when complex computer analysis and simulation codes are involved such as finite element method. Approximation methods are widely used to build simplified approximations, or metamodels providing a surrogate model of the original codes. The most popular surrogate model is the response surface methodology, which typically employs second order polynomial approximation using least-squares regression techniques. Several authors have been used response surface methods in reliability analysis. However, another approximation method based on kriging approach has successfully applied in the field of deterministic optimization. Few studies have treated the use of kriging approximation in reliability analysis and reliability-based design optimization. In this paper, the kriging approximation is used an alternative to the traditional response surface method, to approximate the performance function of the reliability analysis. The main objective of this work is to develop an efficient global approximation while controlling the computational cost and accurate prediction. A pilot point method is proposed to the kriging approximation in order to increase the prior predictivity of the approximation, which the pilot points are good candidates for numerical simulation. In other words, the predictive quality of the initial kriging approximation is improved by adding adaptive information called “pilot points” in areas where the kriging variance is maximum. This methodology allows for an efficient modeling of highly non-linear responses, while the number of simulations is reduced compared to Latin Hypercubes approach. Numerical examples show the efficiency and the interest of the proposed method.
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Parametric estimation of construction cost using combined bootstrap and regre...IAEME Publication
The document discusses a method for estimating construction costs using a combined bootstrap and regression technique. It involves using historical project data to develop a regression model relating cost to key parameters. A bootstrap resampling method is then used to generate multiple simulated datasets from the original. Regression analysis is performed on each resampled dataset to calculate coefficients and develop a cost range estimate that captures uncertainty. This allows integrating probabilistic and parametric estimation methods while requiring fewer assumptions than traditional statistical techniques. The goal is to provide more accurate conceptual cost estimates early in projects when design information is limited.
Quantitative Risk Assessment - Road Development PerspectiveSUBIR KUMAR PODDER
This document outlines an approach for quantitative risk assessment in road transport infrastructure projects using stochastic analysis with triangular distributions. It discusses determining the combined influence of parameters like project cost and traffic on economic indicators. Traditionally, risks from cost and traffic changing from base cases are analyzed separately using triangular distributions defined by minimum, most likely and maximum limits. The document proposes a method to analyze the combined influence of both parameters varying simultaneously using bivariate distributions and conditional probabilities.
This document provides an overview of experimental design, response surface analysis, and optimization. It discusses key terminology like factors, responses, and treatments. Full factorial designs are explained as a way to systematically vary factor levels to estimate main and interaction effects. Response surfaces and metamodels like regression analysis are introduced as tools to approximate simulation responses. Response surface methodology is outlined as a sequential approach combining metamodeling and optimization. Examples help illustrate concepts like estimating effects from a full factorial design. Situations with many factors and terminology in experimental design are also covered at a high level.
This document provides an overview of demand estimation and regression analysis. It discusses how demand estimation is an essential process that informs various business decisions. Regression analysis uses statistical techniques to model the relationship between a dependent variable (e.g. demand) and independent variables (e.g. price, income). Simple regression uses one independent variable, while multiple regression uses more variables. Ordinary least squares is used to estimate the coefficients in the regression equation. These coefficients represent the impact of each independent variable on demand and can be used to forecast demand under different scenarios.
The Sample Average Approximation Method for Stochastic Programs with Integer ...SSA KPI
The document describes a sample average approximation method for solving stochastic programs with integer recourse. It approximates the expected recourse cost function using a sample average based on a sample of scenarios. It shows that as the sample size increases, the solution to the sample average approximation problem converges exponentially fast to the optimal solution of the true stochastic program. It also describes statistical and deterministic techniques for validating candidate solutions. Preliminary computational results applying this method are also mentioned.
Adaptive response surface by kriging using pilot points for structural reliab...IOSR Journals
Structural reliability analysis aims to compute the probability of failure by considering system uncertainties. However, this approach may require very time-consuming computation and becomes impracticable for complex structures especially when complex computer analysis and simulation codes are involved such as finite element method. Approximation methods are widely used to build simplified approximations, or metamodels providing a surrogate model of the original codes. The most popular surrogate model is the response surface methodology, which typically employs second order polynomial approximation using least-squares regression techniques. Several authors have been used response surface methods in reliability analysis. However, another approximation method based on kriging approach has successfully applied in the field of deterministic optimization. Few studies have treated the use of kriging approximation in reliability analysis and reliability-based design optimization. In this paper, the kriging approximation is used an alternative to the traditional response surface method, to approximate the performance function of the reliability analysis. The main objective of this work is to develop an efficient global approximation while controlling the computational cost and accurate prediction. A pilot point method is proposed to the kriging approximation in order to increase the prior predictivity of the approximation, which the pilot points are good candidates for numerical simulation. In other words, the predictive quality of the initial kriging approximation is improved by adding adaptive information called “pilot points” in areas where the kriging variance is maximum. This methodology allows for an efficient modeling of highly non-linear responses, while the number of simulations is reduced compared to Latin Hypercubes approach. Numerical examples show the efficiency and the interest of the proposed method.
IJPR (2015) A Distance-based Methodology for Increased Extraction Of Informat...Nicky Campbell-Allen
This document describes a new methodology for incorporating information from the roof matrices in Quality Function Deployment (QFD) studies. The roof matrices contain correlations between customer requirements (voice of customers) and technical characteristics, but existing methods for including this information in QFD analyses have limitations. The proposed new methodology uses the Manhattan Distance Measure to integrate roof matrix correlation data into the final weightings of technical characteristics. This provides a more consistent way to select technical characteristics by identifying those that are negatively or positively correlated. The methodology is demonstrated using a published QFD case study.
The Use of ARCH and GARCH Models for Estimating and Forecasting Volatility-ru...Ismet Kale
This document discusses volatility modeling using ARCH and GARCH models. It first provides background on ARCH and GARCH models, noting they were developed to model characteristics of financial time series data like volatility clustering and fat tails. It then describes the specific ARCH and GARCH models that will be used in the study, including the ARCH, GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH and FIAPARCH models. The document aims to apply these models to daily stock index data from the IMKB 100 to analyze and forecast volatility, and better understand risk in the Turkish market.
This document summarizes a lecture on binary logistic regression. It begins with an overview of binary logistic regression, noting that it is used to predict a binary categorical outcome variable from predictor variables that may be continuous or categorical. The second segment provides an example using data from mock jury research, with the outcome being a death penalty verdict and predictors being jurors' beliefs. Key outputs of binary logistic regression are explained such as regression coefficients, odds ratios, Wald tests, and measures of model fit and classification success.
Study on Evaluation of Venture Capital Based onInteractive Projection Algorithminventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
This document discusses methods for clustering time series data in a way that allows the cluster structure to change over time. It begins by introducing the problem and defining relevant terms. It then provides spectral clustering as a preliminary benchmark approach before exploring an alternative method using triangular potentials within a graphical model framework. The document presents the proposed method and provides illustrative examples and discussion of extensions.
1) This paper proposes an adaptive quasi-maximum likelihood estimation approach for GARCH models when the distribution of volatility data is unspecified or heavy-tailed.
2) The approach works by using a scale parameter ηf to identify the discrepancy between the wrongly specified innovation density and the true innovation density.
3) Simulation studies and an application show that the adaptive approach gains better efficiency compared to other methods, especially when the innovation error is heavy-tailed.
1) The document analyzes the relationship between stock-commodity correlation and business cycles from 1991-2014 using regression analysis. It finds the stock-commodity correlation is positively related to periods of economic weakness, as evidenced by a positive relationship with default spread.
2) Regression models show stock-commodity correlation is serially correlated and has a negative relationship with default spread, indicating higher correlation during recessions. However, the effect of real GDP growth and inflation on correlation is unclear.
3) In conclusion, the findings are consistent with prior research that stock-commodity correlation increases during economic downturns, when firms adjust behaviors and investor pessimism rises.
This document discusses time series analysis and forecasting of aluminium prices from January 2012 to December 2015. It begins with an introduction to time series concepts and components. It then examines using multiple linear regression and Box-Jenkins methods to model and forecast aluminium prices. Regression analysis found aluminium futures prices highly correlated with prices, but production was not significant. Box-Jenkins is discussed as flexible but identification techniques are difficult and long-term forecasts become straight lines. The document aims to accurately model and forecast future aluminium prices.
A Fuzzy Arithmetic Approach for Perishable Items in Discounted Entropic Order...Waqas Tariq
This paper uses fuzzy arithmetic approach to the system cost for perishable items with instant deterioration for the discounted entropic order quantity model. Traditional crisp system cost observes that some costs may belong to the uncertain factors. It is necessary to extend the system cost to treat also the vague costs. We introduce a new concept which we call entropy and show that the total payoff satisfies the optimization property. We show how special case of this problem reduce to perfect results, and how post deteriorated discounted entropic order quantity model is a generalization of optimization. It has been imperative to demonstrate this model by analysis, which reveals important characteristics of discounted structure. Further numerical experiments are conducted to evaluate the relative performance between the fuzzy and crisp cases in EnOQ and EOQ separately.
[M3A4] Data Analysis and Interpretation SpecializationAndrea Rubio
- The document discusses testing a logistic regression model with a binary response variable (trouble paying attention in school) and multiple explanatory variables using data from the AddHealth dataset.
- A logistic regression model is created with "NOBREAKFAST" as the single explanatory variable, finding students with no breakfast are 1.37 times more likely to have trouble paying attention.
- A second model adds the variable "ENOUGHSLEEP", finding enough sleep reduces the likelihood by a factor of 0.44.
- A third full model is created to check for confounding, but findings remain consistent with no breakfast increasing the likelihood of trouble paying attention.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Qm0019 foundations of quality managementsmumbahelp
This document provides information about getting fully solved assignments for the MBA semester 3 subject Foundations of Quality Management. It includes details like the semester, subject code, credit hours, and contact information to email or call for assistance. The document then provides a sample assignment question and answers related to topics in quality management, including definitions of total quality management, contributions of quality gurus, quality policy and objectives, quality audits, productivity, knowledge management, quality awards, and types of quality costs.
El documento habla sobre diferentes tipos de virus, fraudes y amenazas a la seguridad informática. Define términos como antivirus, cifrado, ciberdelincuencia, cracker, correo electrónico, copias de seguridad, firewall, hacker, hacktivismo, keylogger, phishing, ransomware, rogueware, spam, gusano, malware, software, spyware, suplantación de identidad, troyano, virus, vulnerabilidades y webs maliciosas.
This document is a resume for Megharaja Pedde seeking a software development position. It summarizes his objective, qualifications, experience, skills, education and technical projects. He has over 4 years of experience in software development using technologies like Delphi, Oracle and SQL. His most recent role is as an Associate Project at Cognizant developing applications like Stanchion for inventory management and sales tracking.
Homework from my Mandarin class.
Little approach into Vietnam culture, specifically Vietnam Moon Festival - a national occasional day.
Through this project, hopefully Chinese-learning audiences may have a better understanding about Vietnam Moon Festival Day.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for both physical and mental health. Regular exercise can improve cardiovascular health, reduce symptoms of depression and anxiety, enhance mood, and boost brain health. Staying physically active aims to reap these rewards by incorporating exercise into a daily routine.
IJPR (2015) A Distance-based Methodology for Increased Extraction Of Informat...Nicky Campbell-Allen
This document describes a new methodology for incorporating information from the roof matrices in Quality Function Deployment (QFD) studies. The roof matrices contain correlations between customer requirements (voice of customers) and technical characteristics, but existing methods for including this information in QFD analyses have limitations. The proposed new methodology uses the Manhattan Distance Measure to integrate roof matrix correlation data into the final weightings of technical characteristics. This provides a more consistent way to select technical characteristics by identifying those that are negatively or positively correlated. The methodology is demonstrated using a published QFD case study.
The Use of ARCH and GARCH Models for Estimating and Forecasting Volatility-ru...Ismet Kale
This document discusses volatility modeling using ARCH and GARCH models. It first provides background on ARCH and GARCH models, noting they were developed to model characteristics of financial time series data like volatility clustering and fat tails. It then describes the specific ARCH and GARCH models that will be used in the study, including the ARCH, GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH and FIAPARCH models. The document aims to apply these models to daily stock index data from the IMKB 100 to analyze and forecast volatility, and better understand risk in the Turkish market.
This document summarizes a lecture on binary logistic regression. It begins with an overview of binary logistic regression, noting that it is used to predict a binary categorical outcome variable from predictor variables that may be continuous or categorical. The second segment provides an example using data from mock jury research, with the outcome being a death penalty verdict and predictors being jurors' beliefs. Key outputs of binary logistic regression are explained such as regression coefficients, odds ratios, Wald tests, and measures of model fit and classification success.
Study on Evaluation of Venture Capital Based onInteractive Projection Algorithminventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
This document discusses methods for clustering time series data in a way that allows the cluster structure to change over time. It begins by introducing the problem and defining relevant terms. It then provides spectral clustering as a preliminary benchmark approach before exploring an alternative method using triangular potentials within a graphical model framework. The document presents the proposed method and provides illustrative examples and discussion of extensions.
1) This paper proposes an adaptive quasi-maximum likelihood estimation approach for GARCH models when the distribution of volatility data is unspecified or heavy-tailed.
2) The approach works by using a scale parameter ηf to identify the discrepancy between the wrongly specified innovation density and the true innovation density.
3) Simulation studies and an application show that the adaptive approach gains better efficiency compared to other methods, especially when the innovation error is heavy-tailed.
1) The document analyzes the relationship between stock-commodity correlation and business cycles from 1991-2014 using regression analysis. It finds the stock-commodity correlation is positively related to periods of economic weakness, as evidenced by a positive relationship with default spread.
2) Regression models show stock-commodity correlation is serially correlated and has a negative relationship with default spread, indicating higher correlation during recessions. However, the effect of real GDP growth and inflation on correlation is unclear.
3) In conclusion, the findings are consistent with prior research that stock-commodity correlation increases during economic downturns, when firms adjust behaviors and investor pessimism rises.
This document discusses time series analysis and forecasting of aluminium prices from January 2012 to December 2015. It begins with an introduction to time series concepts and components. It then examines using multiple linear regression and Box-Jenkins methods to model and forecast aluminium prices. Regression analysis found aluminium futures prices highly correlated with prices, but production was not significant. Box-Jenkins is discussed as flexible but identification techniques are difficult and long-term forecasts become straight lines. The document aims to accurately model and forecast future aluminium prices.
A Fuzzy Arithmetic Approach for Perishable Items in Discounted Entropic Order...Waqas Tariq
This paper uses fuzzy arithmetic approach to the system cost for perishable items with instant deterioration for the discounted entropic order quantity model. Traditional crisp system cost observes that some costs may belong to the uncertain factors. It is necessary to extend the system cost to treat also the vague costs. We introduce a new concept which we call entropy and show that the total payoff satisfies the optimization property. We show how special case of this problem reduce to perfect results, and how post deteriorated discounted entropic order quantity model is a generalization of optimization. It has been imperative to demonstrate this model by analysis, which reveals important characteristics of discounted structure. Further numerical experiments are conducted to evaluate the relative performance between the fuzzy and crisp cases in EnOQ and EOQ separately.
[M3A4] Data Analysis and Interpretation SpecializationAndrea Rubio
- The document discusses testing a logistic regression model with a binary response variable (trouble paying attention in school) and multiple explanatory variables using data from the AddHealth dataset.
- A logistic regression model is created with "NOBREAKFAST" as the single explanatory variable, finding students with no breakfast are 1.37 times more likely to have trouble paying attention.
- A second model adds the variable "ENOUGHSLEEP", finding enough sleep reduces the likelihood by a factor of 0.44.
- A third full model is created to check for confounding, but findings remain consistent with no breakfast increasing the likelihood of trouble paying attention.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Qm0019 foundations of quality managementsmumbahelp
This document provides information about getting fully solved assignments for the MBA semester 3 subject Foundations of Quality Management. It includes details like the semester, subject code, credit hours, and contact information to email or call for assistance. The document then provides a sample assignment question and answers related to topics in quality management, including definitions of total quality management, contributions of quality gurus, quality policy and objectives, quality audits, productivity, knowledge management, quality awards, and types of quality costs.
El documento habla sobre diferentes tipos de virus, fraudes y amenazas a la seguridad informática. Define términos como antivirus, cifrado, ciberdelincuencia, cracker, correo electrónico, copias de seguridad, firewall, hacker, hacktivismo, keylogger, phishing, ransomware, rogueware, spam, gusano, malware, software, spyware, suplantación de identidad, troyano, virus, vulnerabilidades y webs maliciosas.
This document is a resume for Megharaja Pedde seeking a software development position. It summarizes his objective, qualifications, experience, skills, education and technical projects. He has over 4 years of experience in software development using technologies like Delphi, Oracle and SQL. His most recent role is as an Associate Project at Cognizant developing applications like Stanchion for inventory management and sales tracking.
Homework from my Mandarin class.
Little approach into Vietnam culture, specifically Vietnam Moon Festival - a national occasional day.
Through this project, hopefully Chinese-learning audiences may have a better understanding about Vietnam Moon Festival Day.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for both physical and mental health. Regular exercise can improve cardiovascular health, reduce symptoms of depression and anxiety, enhance mood, and boost brain health. Staying physically active aims to reap these rewards by incorporating exercise into a daily routine.
Application Whitelisting - Complementing Threat centric with Trust centric se...Osama Salah
This document discusses application whitelisting as a security control that can complement traditional threat-centric security approaches. It notes that application whitelisting works on a principle of default deny by only allowing approved applications to run, whereas traditional antivirus uses a default allow approach. The document outlines challenges with traditional antivirus, including its inability to keep up with the exponential growth of malware. It advocates for implementing application whitelisting to prevent both known and unknown threats from executing. Key considerations for implementation include scope, stakeholder engagement, approval processes, and change management. The document argues that application whitelisting can significantly reduce malware incidents when implemented effectively.
Es una filosofía que define la forma en que debería optimizarse un sistema de producción. Se trata de entregar materias primas o componentes a la línea de fabricación de forma que lleguen “justo a tiempo” a medida que son necesarios.
Es una filosofía que define la forma en que debería optimizarse un sistema de producción. Se trata de entregar materias primas o componentes a la línea de fabricación de forma que lleguen “justo a tiempo” a medida que son necesarios.
Cosmétologie : Les impacts des biotechnologies en cosmétique.Réseau Pro Santé
Revue "Pharmacien demain" n°26 - ALEE-
Les biotechnologies sont des industries employant des techniques utilisant des êtres vivants, micro-organismes, animaux, ou végétaux, généralement après modification de leurs caractéristiques génétiques, pour la fabrication industrielle de composés biologiques ou chimiques comme des médicaments, matières premières industrielles ou pour l’amélioration de la production agricole comme les plantes et ou les animaux transgéniques ou O.G.M. [organismes génétiquement modifiés]
Ainsi, la biotechnologie est utilisée dans les industries pharmaceutique, agricole, chimique mais aussi dans l’industrie cosmétique.
En effet, la biotechnologie est une nouvelle source d’innovation pour la cosmétologie. Même si elle est encore un peu plus chère que les techniques issues de la chimie classique, la biotechnologie monte en puissance depuis une quinzaine d’années.
Comme dans l’industrie pharmaceutique, la biotechnologie est capable de synthétiser des molécules qui auront potentiellement une activité biologique transcutanée, lorsque la chimie lourde n’y parvient pas toujours.
Chez Solabia, on fabrique chaque année plusieurs dizaines de tonnes de molécules à des coûts compatibles avec le marché, grâce à des micro-organismes placés dans un fermenteur. Ce procédé n’est pas encore abordable pour fabriquer en très grande quantité des molécules destinées à des marchés de grand volume comme les biocarburants ou la chimie, mais présente un avenir très prometteur !
reseauprosante.fr
Es una filosofía que define la forma en que debería optimizarse un sistema de producción. Se trata de entregar materias primas o componentes a la línea de fabricación de forma que lleguen “justo a tiempo” a medida que son necesarios.
This document provides instructions for students to obtain fully solved assignments. It tells students to send their semester and specialization name to the email address "help.mbaassignments@gmail.com" or call the phone number 08263069601 to receive solved assignments. It provides this contact information to help students complete their coursework.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides information about obtaining fully solved assignments for the SMU BBA Spring 2014 semester. Students are instructed to send their semester and specialization name to the provided email address or call the given phone number to receive assistance with assignments. Details are provided about available assignments in subjects like CRM and IT in Banking, SAP, business process reengineering, e-cheques in India, data mining applications in banks, and information system security requirements for banks.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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Operations Management VTU BE Mechanical 2015 Solved paperSomashekar S.M
The document provides information about operations management concepts including scientific management, productivity, ABC analysis, economic order quantity, and materials requirements planning. It defines each concept and provides examples to illustrate how they are applied. Scientific management aims to improve efficiency through systematic analysis of work processes. Productivity is a measure of output per unit of input. ABC analysis categorizes inventory items based on their value and usage to determine appropriate control methods. Economic order quantity and ordering cycle determine optimal replenishment amounts and frequencies to minimize total inventory costs. Materials requirements planning is a technique to plan material needs at different production levels based on a product structure tree.
QM0012- STATISTICAL PROCESS CONTROL AND PROCESS CAPABILITYsmumbahelp
This document provides information about a fully solved assignment for students. It lists the semester, specialization, subject code, name, credits, and marks. It also provides 6 questions related to statistical process control and process capability. For each question, it provides the evaluation scheme and space to write the answer. The questions cover topics like Pareto charts, scatter diagrams, Poisson distribution, hypothesis testing, analysis of variance, attribute control charts, and the methodology for statistical process control implementation.
A CCP is an experienced practitioner with advanced knowledge and technical expertise to apply the broad principles and best practices of Total Cost Management (TCM) in the planning, execution and management of any organizational project or program. CCPs also demonstrate the ability to research and communicate aspects of TCM principles and practices to all levels of project or program stakeholders, both internally and externally.
ENTROPY-COST RATIO MAXIMIZATION MODEL FOR EFFICIENT STOCK PORTFOLIO SELECTION...cscpconf
This paper introduces a new stock portfolio selection model in non-stochastic environment.Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as
the objective function. The uncertain returns, risks and ividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future inancial market optimistically, pessimistically and in hecombined form to model the portfolio selection problem. To illustrate the effectiveness and tractability of the proposed models, these are tested on a set of data from Bombay Stock Exchange (BSE). The solution has been done by genetic algorithm.
An Empirical Investigation Of The Arbitrage Pricing TheoryAkhil Goyal
The study empirically tests the Arbitrage Pricing Theory (APT) developed by Ross in 1976 using daily stock return data from 1962-1972. It finds:
1) Factor analysis identifies 5 factors that explain stock returns within industry groups, supporting the APT.
2) Cross-sectional regressions show factor loadings can explain expected stock returns, as the APT predicts.
3) Adding total return variance to the regressions does not eliminate the explanatory power of factor loadings, supporting the APT over alternatives.
4) Tests across industry groups find no evidence factor structures differ, as the APT assumes consistent factors across stocks.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
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call us at : 08263069601
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This document provides an overview and categorization of various marketing research techniques. It separates the techniques into mature techniques that have been used for some time, such as correlation analysis and regression analysis, and modern techniques that are newer, such as decision trees, dynamic programming, and technological forecasting. For several of the techniques, a brief explanation of the approach is given. The overall purpose is to familiarize management with the key research tools used by researchers.
The document discusses dimensional analysis and Buckingham Pi theorem. It begins by defining dimensions, units, and fundamental vs. derived dimensions. It then discusses dimensional homogeneity and uses examples to show how dimensional analysis can be used to identify non-dimensional parameters and reduce the number of variables in equations. The Buckingham Pi theorem is introduced as a method to systematically create dimensionless pi terms from physical variables. Steps of the theorem and examples applying it are provided. Overall, the document provides an overview of dimensional analysis and Buckingham Pi theorem as tools for understanding relationships between physical quantities and reducing complexity in experimental modeling.
Qm0012 statistical process control and process capabilitysmumbahelp
This document provides fully solved assignments for the MBA semester 3 course "QM0012 - Statistical Process Control and Process Capability". It includes 6 questions related to statistical process control, cause and effect diagrams, control charts, experimental design, process capability, and acceptance sampling. Students can send their semester and specialization details to the provided email or call the phone number to receive the solved assignments. The questions cover topics such as the meaning and differences between statistical quality control and statistical process control, explaining the structure and construction of control charts with examples, guidelines for experimental design and acceptance sampling, and more.
1. Demand forecasting is used to estimate future demand for products over specific time periods and is important for planning operations.
2. Demand can be categorized by the type of goods (consumer vs capital) and time period (short, medium, long term). Quantitative forecasting techniques include trend projection methods like time series analysis and regression.
3. Techniques like ARIMA combine moving averages and autoregressive methods to model trends and differences in time series data. Regression analysis uses statistical methods to model relationships between demand and influencing factors.
This document discusses static and dynamic models, deterministic and stochastic models, and various methods for studying systems with uncertainty. Deterministic models use differential equations to exactly predict outcomes, while stochastic models use random variables and can only compute probabilities. Numerical methods and simulation are introduced as ways to study more complex systems. Simulation models represent real systems and allow experiments to be performed faster and safer. Monte Carlo methods and discrete event simulation are discussed as techniques for simulation.
www.elsevier.comlocatecompstrucComputers and Structures .docxjeffevans62972
www.elsevier.com/locate/compstruc
Computers and Structures 85 (2007) 235–243
On the treatment of uncertainties in structural mechanics and analysis q
G.I. Schuëller *
Institute of Engineering Mechanics, Leopold-Franzens University Innsbruck, Technikerstr. 13, 6020 Innsbruck, Austria
Received 9 August 2006; accepted 31 October 2006
Available online 22 December 2006
Abstract
In this paper the need for a rational treatment of uncertainties in structural mechanics and analysis is reasoned. It is shown that the
traditional deterministic conception can be easily extended by applying statistical and probabilistic concepts. The so-called Monte Carlo
simulation procedure is the key for those developments, as it allows the straightforward use of the currently used deterministic analysis
procedures.
A numerical example exemplifies the methodology. It is concluded that uncertainty analysis may ensure robust predictions of vari-
ability, model verification, safety assessment, etc.
� 2006 Elsevier Ltd. All rights reserved.
Keywords: Uncertainty; Monte Carlo simulaton; Finite elements; Response variability; Model verification; Robustness
1. Introduction
Structural mechanics analysis up to this date, generally is
still based on a deterministic conception. Observed varia-
tions in loading conditions, material properties, geometry,
etc. are taken into account by either selecting extremely
high, low or average values, respectively, for representing
the parameters. Hence, this way, uncertainties inherent in
almost every analysis process are considered just intuitively.
Observations and measurements of physical processes,
however, show not only variability, but also random char-
acteristics. Statistical and probabilistic procedures provide
a sound frame work for a rational treatment of analysis
of these uncertainties. Moreover there are various types of
uncertainties to be dealt with. While the uncertainties in
mechanical modeling can be reduced as additional knowl-
edge becomes available, the physical or intrinsic uncertain-
ties, e.g. of environmental loading, can not. Furthermore,
0045-7949/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compstruc.2006.10.009
q Plenary Keynote Lecture presented at the 3rd MIT Conference on
Computational Fluid and Solid Mechanics, Boston, MA, USA, June 14–
17, 2005.
* Tel.: +43 512 507 6841; fax: +43 512 507 2905.
E-mail address: [email protected]
the entire spectrum of uncertainties is also not known. In
reality, neither the true model nor the model parameters
are deterministically known. Assuming that by finite ele-
ment (FE) procedures structures and continua can be repre-
sented reasonably well the question of the effect of the
discretization still remains. It is generally expected, that
an increase in the size of the structural models, in terms of
degrees of freedom, will increase the level of realism of the
model. Comparisons with measurements, however, clearly
show that this expect.
Econometrics uses economic theory and statistical tools to quantify economic relationships and answer questions about economic data. An econometric model includes both a systematic component based on economic theory and an error term that represents unpredictable factors. Most economic data comes from non-experimental sources and is in time-series, cross-sectional, or panel form. The goal of econometrics is statistical inference like estimating parameters, predicting outcomes, and testing hypotheses using sample data. Econometric models incorporate probability distributions, random variables, and concepts like the mean, variance, and normal distribution to analyze economic data statistically.
This research paper demonstrates the invention of the kinetic bands, based on Romanian mathematician and statistician Octav Onicescu’s kinetic energy, also known as “informational energy”, where we use historical data of foreign exchange currencies or indexes to predict the trend displayed by a stock or an index and whether it will go up or down in the future. Here, we explore the imperfections of the Bollinger Bands to determine a more sophisticated triplet of indicators that predict the future movement of prices in the Stock Market. An Extreme Gradient Boosting Modelling was conducted in Python using historical data set from Kaggle, the historical data set spanning all current 500 companies listed. An invariable importance feature was plotted. The results displayed that Kinetic Bands, derived from (KE) are very influential as features or technical indicators of stock market trends. Furthermore, experiments done through this invention provide tangible evidence of the empirical aspects of it. The machine learning code has low chances of error if all the proper procedures and coding are in play. The experiment samples are attached to this study for future references or scrutiny.
Principles of design of experiments (doe)20 5-2014Awad Albalwi
This document discusses experimental design and optimization. It defines key terms like factors, responses, and residuals. It explains that experimental design is used to systematically examine problems in research, development and production. Factorial design is introduced as a method to study the effects of all factors and interactions on responses. The document provides an example experimental design to investigate if playing violent video games causes violent behavior. It outlines defining the population, randomly selecting a sample, using control and experimental conditions, measuring dependent variables, and comparing results to draw conclusions.
This document provides an overview and examples of various statistical concepts and tools, including:
- Useful statistical measures such as mean, median, mode, range, variance, and standard deviation.
- The normal distribution and how to calculate proportions of values that fall within a certain range using normal distribution tables or Excel functions.
- Common values from the normal distribution such as what proportion of values fall within 1, 2, or 3 standard deviations of the mean.
- Six Sigma "sigma values" and how they correspond to defects per million opportunities.
- Visualization tools like histograms, Pareto charts, stem-and-leaf plots, scatter graphs, multi-vari charts, and box plots; including
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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Qm0021 statistical process control
1. Dear students get fully solved assignments
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Assignment
DRIVE FALL 2015
PROGRAM MBA
SEMESTER 3
SUBJECT CODE & NAME QM0021: Statistical ProcessControl
BK ID B1928
CREDIT & MARKS 4 CREDITS & 60 MARKS
Note:Answer all questions.Each questionisfollowedby evaluationscheme.
1(a) What are the two main causes of variation?Explain.
Answer: Twocausesof variation:
There are two forms of variation: continuous and discontinuous variation. Characteristics showing
continuous variation vary in a general way, with a broad range, and many intermediate values between
the extremes. As a matter of fact, if you consider a large enough sample from a population, perhaps
plottingfrequencyasa histogramor as a
(b) Define the term ‘processes.Give an example ofprocess.
Answer: Sequence of interdependent and linked procedures which, at every stage, consume one or
more resources (employee time,energy, machines, money) to convert inputs (data, material, parts, etc.)
into outputs. These outputs then serve as inputs for the next stage until a known goal or end result is
reached.
2(a) What is meant by Standard Deviation?
Answer: In statistics and probability theory, the standard deviation (SD) (represented by the Greek letter
sigma, σ) measures the amount of variation or dispersion from the average. A low standard deviation
indicatesthatthe data pointstend
2. (b) Calculate the standard deviation of the following data, which represents the number of defective
products by a machine:4, 2, 5, 8 and 6
Answer:
Total Numbers:5
Mean (Average):5
Standarddeviation: 2.
3a) Give the meaningof the followingbasicterminologiesin Probability:
(i) Sample Space
Answer: The sample space of an experiment or random trial is the set of all possible outcomes or results
of that experiment.A sample space is usually denoted using set notation, and the possible outcomes are
listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for
"universal set").
ii) MutuallyExclusive events
Answer: Two events are mutually exclusive if they cannot occur at the same time. An example is tossing
a coin once,which can resultineitherheadsortails,butnotboth.
In the coin-tossingexample,
b) Mentionthe propertiesof probability
Answer:
Property 1:
If A is an outcome ina sample space S,then
P(A) > 0
Property 2:
c) Define the term ‘random variable’
Answer: A random variable, aleatory variable or stochastic variable is a variable whose value is subject
to variations due to chance .A random variable can take on a set of possible different values (similarly to
other mathematical variables), each with an associated probability, in contrast to other mathematical
variables.
3. 4(a) Differentiate betweenaccuracyand precision.
Answer: Accuracy and precision are defined in terms of systematic and random errors. The more
common definition associates accuracy with systematic errors and precision with random errors.
Another definition, advanced by ISO, associates trueness with systematic errors and precision with
randomerrors,and definesaccuracyas the combinationof bothtruenessandprecision.
A measurementsystemcanbe
(b) Write a briefnote on ‘Funnel Experiment’
Answer: The Funnel Experiment was devised by Dr. Deming to describe the adverse effects of tampering
with a process by making changes to it without first making a careful study of the possible causes of the
variationinthatprocess.
In the experiment, a marble is dropped through a funnel onto a sheet of paper, which contains a target.
The objective of the process is to get the marble to come to a stop as close to the target as possible. The
experiment uses several methods to attempt to manipulate the funnel’s location to achieve the
objective.
5 Define the terms: ‘processcapability’and ‘processstability’.ExplainCp indexand Cpk index.
Answer: A process is a unique combination of tools, materials, methods, and people engaged in
producing a measurable output; for example a manufacturing line for machine parts. All processes have
inherentstatistical variabilitywhichcanbe evaluatedbystatisticalmethods.
The Process Capability is a measurable property of a process to the specification, expressed as a process
capability index (e.g., Cpk or Cpm) or as a process performance index (e.g., Ppk or Ppm). The output of
thismeasurementisusuallyillustratedby
6. Give the meaningof the following:
i) Hypothesistesting
Answer: A statistical hypothesis is a scientific hypothesis that is testable on the basis of observing a
process that is modeled via a set of random variables. A statistical hypothesis test is a method of
statistical inference usedfortestingastatistical hypothesis.
A test result is called statistically significant if it has been predicted as unlikely to have occurred by
chance alone,accordingto a
ii) Control chart
4. Answer: Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior
charts, instatistical
iii) Experimental design
Answer: In an experiment, we deliberately change one or more process variables (or factors) in order to
observe the effectthe changes
iv) Acceptance Sampling
Answer: Acceptance sampling uses statistical sampling to determine whether to accept or reject a
production lot of material. It has been a common quality control technique used in industry. It is usually
done as products leave the factory, or in some cases even within the factory. Most often a producer
suppliesaconsumer
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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