India is the world’s largest two-wheeler market and despite the losses suffered by bike makers in India during the shift to BS-IV, the industry recovered well with a massive growth of 14.80 percent. Overall the two-wheeler industry saw a total sales of over 2.01 cr. units sold in the Indian domestic market and exports from the country also shot up by over 20 percent. Within the Two Wheelers segment, Motorcycles grew by 13.69 percent respectively.(Financial Express). From that report we can conclude that every 5th person owns a 2-wheeler and it is a vast market. So we try to understand the decision making part which done before buy a bike. We took five criteria to which helps customers to take decision and five alternative choice from which customer can choose their final product.
The document summarizes research on consumer buying behavior for new cars. It describes the research objectives, questionnaire design, sample characteristics of customers from 5 major car companies in India, and data analysis using ANOVA, regression, and factor analysis. The analysis found significant differences between the 5 car companies and that the regression model was not a good fit. Factor analysis yielded a KMO value indicating poor common variance. The conclusion discusses findings on timing of orders and influences on men and women buyers.
Multiple Linear Regression Applications Automobile Pricinginventionjournals
This document describes using multiple linear regression to predict automobile prices. The response variable is price from Kelley Blue Book for 470 cars. Potential explanatory variables are mileage, make, type, liter size, cruise control, upgraded speakers, and leather seats. Preliminary analysis finds mileage and liter have significant correlations with price. The final regression model finds price is best predicted by an equation involving liter size and mileage as the most important factors. The model explains over 80% of price variation and provides a way for buyers and sellers to estimate reasonable car prices.
The document discusses various methods for scaling in marketing research, including nominal, ordinal, interval, and ratio scales. It then compares comparative scaling techniques like paired comparisons and rank ordering, which involve direct comparisons between items, to noncomparative techniques like Likert scales that measure items independently. Finally, it provides examples of using paired comparisons, rank ordering, and constant sum scaling to measure preferences.
Measurement and scaling fundamentals and comparative scalingRohit Kumar
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
The document summarizes research on consumer buying behavior for new cars. It describes the research objectives, questionnaire design, sample characteristics of customers from 5 major car companies in India, and data analysis using ANOVA, regression, and factor analysis. The analysis found significant differences between the 5 car companies and that the regression model was not a good fit. Factor analysis yielded a KMO value indicating poor common variance. The conclusion discusses findings on timing of orders and influences on men and women buyers.
Multiple Linear Regression Applications Automobile Pricinginventionjournals
This document describes using multiple linear regression to predict automobile prices. The response variable is price from Kelley Blue Book for 470 cars. Potential explanatory variables are mileage, make, type, liter size, cruise control, upgraded speakers, and leather seats. Preliminary analysis finds mileage and liter have significant correlations with price. The final regression model finds price is best predicted by an equation involving liter size and mileage as the most important factors. The model explains over 80% of price variation and provides a way for buyers and sellers to estimate reasonable car prices.
The document discusses various methods for scaling in marketing research, including nominal, ordinal, interval, and ratio scales. It then compares comparative scaling techniques like paired comparisons and rank ordering, which involve direct comparisons between items, to noncomparative techniques like Likert scales that measure items independently. Finally, it provides examples of using paired comparisons, rank ordering, and constant sum scaling to measure preferences.
Measurement and scaling fundamentals and comparative scalingRohit Kumar
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
This document discusses modeling returns and volatility of the Shanghai Stock Exchange using a time series model to forecast future returns and risk for investing in the Shanghai Pilot Free Trade Zone. The author constructs several ARMA models using log returns of the stock index from 2000 to 2014, selecting optimal models based on Akaike and Bayesian Information Criteria. Diagnostics are performed on residuals to check assumptions and select models that capture volatility. The top three models identified are ARMA(5,4), ARMA(1,1), and ARMA(2,3).
Multi criteria decision support system on mobile phone selection with ahp and...Reza Ramezani
This document proposes using multi-criteria decision making (MCDM) approaches, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to help users select a mobile phone. It outlines the evaluation process, which involves identifying important mobile phone selection criteria, calculating criteria weights using AHP, and then using TOPSIS to rank mobile phone alternatives based on how close they are to an ideal solution and how far they are from a negative ideal solution. The document provides examples of building pairwise comparison matrices in AHP and calculating ideal and non-ideal solutions and alternative distances in TOPSIS to demonstrate the selection approach.
Steven K Allott - Effective Testing - SoftTest IrelandDavid O'Dowd
The document discusses effective testing techniques, including risk-based testing and black box techniques like equivalence partitioning, boundary value analysis, decision tables, and state transition testing. It provides examples of how to use these techniques to design test cases and reduce the number of tests needed while improving coverage. Pairwise testing is presented as a technique to further reduce the number of test cases by testing variable combinations rather than all combinations.
The Supply Chains To Admire Report for 2021Lora Cecere
The Supply Chains To Admire methodology tracks the rate of improvement and performance of 60 public companies in 28 industry sectors. Twenty companies outperform including In the 2021 analysis, twenty companies met the Supply Chains to Admire Award criteria. The winners include Apple, AbbVie Inc., Air Products & Chemicals, Assa Abloy AB, Broadcom, Celestica, Dollar General, Ecolab Inc., Intuitive Surgical, Inditex, Lockheed Martin Corporation, Nike Inc., Nvidia, PACCAR Inc, Ross Stores, Sleep Number, Taiwan Semiconductor Manufacturing (TSMC) Company, Tempur Sealy, TJX Companies, and Western Digital. No company met the criteria in seventeen of the twenty-six sectors studied.
IRJET-An Entropy-Weight Based TOPSIS Approach for Supplier SelectionIRJET Journal
This document presents a methodology for supplier selection using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method combined with entropy weight. It involves using a decision matrix to evaluate suppliers based on multiple quantitative and qualitative criteria. The weights of each criterion are determined using an entropy weight calculation. The TOPSIS method is then applied to rank suppliers based on their distance from the ideal positive and negative solutions. An illustrative example is provided to demonstrate the step-by-step calculations for applying this methodology to a supplier selection problem involving four suppliers and five evaluation criteria.
The document describes using the Analytic Hierarchy Process (AHP) to help a couple choose which car to purchase out of three options: Honda, Mazda, or Volvo. It outlines the steps to build an AHP model in Decision Lens software, including establishing criteria of cost, safety, and appearance to evaluate the alternatives. Pairwise comparisons are made between criteria to calculate their weights, and between alternatives for each criterion. The final weights show the Honda as the top choice.
The document describes using the Analytic Hierarchy Process (AHP) in the Decision Lens software to help a couple select the best car to purchase. It walks through building an AHP model in Decision Lens to evaluate three criteria (cost, safety, appearance) and three car alternatives (Honda, Mazda, Volvo). It provides an example of pairwise comparing the criteria, calculating the criteria weights, and discusses entering judgments directly in Decision Lens.
This document provides instructions for how to perform Analytic Hierarchy Process (AHP) analysis in Excel. It discusses developing a hierarchy of decision criteria and alternatives, making pairwise comparisons between elements, normalizing the comparisons to generate weights, and checking consistency. The basic steps are outlined as developing pairwise comparison matrices, normalizing the matrices to obtain weights, and calculating a consistency ratio to validate the judgments.
This document provides an overview of the analytical hierarchy process (AHP), a multiple-criteria decision analysis technique for evaluating project alternatives. AHP involves structuring the decision problem into a hierarchy, measuring priorities through pairwise comparisons, and synthesizing the results. The document includes an example application of AHP and discusses its use in a case study evaluating design options for a grade separation on the California High-Speed Rail project.
The document discusses predicting matches in speed dating using machine learning models. It summarizes the steps taken which include:
1) Cleaning and exploring the speed dating dataset to engineer relevant features like combined interests and age differences.
2) Initial modeling with random forest, logistic regression, SVC and gradient boosting classifiers showed underfitting.
3) Feature selection using random forest importance improved results by focusing on combined interest features.
4) Parameter tuning of random forest and SVC classifiers further optimized performance, with the best models achieving over 95% accuracy.
Hiring- One of the essential aspects of the corporate sector is the most concerning and nerve-wracking area for every organization and to give everyone freedom from stress Gartner Digital Markets comes up with the list of top performing recruitment software every year.
A recruitment software's basic function is to electronically automate the job posting on multiple channels, creating visual hiring pipelines, filtering applications, shortlisting, interview scheduling and helps you in streamlining the recruitment process.
‘Recooty’ – a modern recruitment software company has emerged as a game changer this year, which helps you attract & engage great talents easily.
To create this report, Software Advice evaluated over 320 Applicant Tracking System (ATS). Only those with the top scores for Usability and UserRecommended made the cut as FrontRunners.
Out of all Global Recruitment Software products reviewed by Software Advice in 2019, Recooty scored highest 4.95 out of 5 and emerged the clear recruitment applicant tracking system market leader based on verified user reviews and ratings. Vendors include Recooty, Workable, Bullhorn, Taleo and more.
Econometrics beat dave giles' blog ardl modelling in e_views 9b1mit
This blog post discusses autoregressive distributed lag (ARDL) modelling in EViews 9. Specifically:
1) The post demonstrates how to estimate an ARDL model to examine the relationship between gasoline and crude oil prices in Canada using weekly data from 2000 to 2013.
2) Unit root tests on the logged price series find inconclusive evidence of non-stationarity, making ARDL modelling appropriate.
3) An ARDL model is estimated with lags of the dependent and independent variables as regressors, selected using information criteria.
4) The bounds test allows examination of the long-run relationship between gasoline and crude oil prices.
Why is Sales and Operations Planning So Hard?Lora Cecere
Sales and Operations Planning processes are not a panacea. Just because an organization has a process, does not automatically mean that the company will drive value.
In the past decade, company progress moved backwards with fewer and fewer companies believing that they are successful. The reasons? Lack of definition of supply chain excellence, the need for design, clear delineation of governance, clarity of the role of the financial budget and the organizational tension in reconciliation with the market, and the lack of organizational alignment.
The document discusses various techniques for analyzing brands, including:
1) Understanding industries and competitors to analyze market share, price tiers, and marketing strategies.
2) Using revealed and stated preference data to understand consumer decision-making and brand perceptions.
3) Conducting factor analysis to reduce many brand attributes to a few underlying factors that capture most of the information, which can then be used to position brands on perceptual maps.
This document discusses various techniques for analyzing brands, including understanding industry structure, customer and competitor insights, and different approaches to positioning brands. It covers eliciting brand perceptions through stated preference surveys and revealed preference data. Factor analysis and clustering are presented as methods to understand the underlying dimensions that drive consumer perceptions of brands and group brands accordingly. Perceptual maps can be created by plotting brand locations based on their factor scores to visualize positioning.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
This document discusses modeling returns and volatility of the Shanghai Stock Exchange using a time series model to forecast future returns and risk for investing in the Shanghai Pilot Free Trade Zone. The author constructs several ARMA models using log returns of the stock index from 2000 to 2014, selecting optimal models based on Akaike and Bayesian Information Criteria. Diagnostics are performed on residuals to check assumptions and select models that capture volatility. The top three models identified are ARMA(5,4), ARMA(1,1), and ARMA(2,3).
Multi criteria decision support system on mobile phone selection with ahp and...Reza Ramezani
This document proposes using multi-criteria decision making (MCDM) approaches, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to help users select a mobile phone. It outlines the evaluation process, which involves identifying important mobile phone selection criteria, calculating criteria weights using AHP, and then using TOPSIS to rank mobile phone alternatives based on how close they are to an ideal solution and how far they are from a negative ideal solution. The document provides examples of building pairwise comparison matrices in AHP and calculating ideal and non-ideal solutions and alternative distances in TOPSIS to demonstrate the selection approach.
Steven K Allott - Effective Testing - SoftTest IrelandDavid O'Dowd
The document discusses effective testing techniques, including risk-based testing and black box techniques like equivalence partitioning, boundary value analysis, decision tables, and state transition testing. It provides examples of how to use these techniques to design test cases and reduce the number of tests needed while improving coverage. Pairwise testing is presented as a technique to further reduce the number of test cases by testing variable combinations rather than all combinations.
The Supply Chains To Admire Report for 2021Lora Cecere
The Supply Chains To Admire methodology tracks the rate of improvement and performance of 60 public companies in 28 industry sectors. Twenty companies outperform including In the 2021 analysis, twenty companies met the Supply Chains to Admire Award criteria. The winners include Apple, AbbVie Inc., Air Products & Chemicals, Assa Abloy AB, Broadcom, Celestica, Dollar General, Ecolab Inc., Intuitive Surgical, Inditex, Lockheed Martin Corporation, Nike Inc., Nvidia, PACCAR Inc, Ross Stores, Sleep Number, Taiwan Semiconductor Manufacturing (TSMC) Company, Tempur Sealy, TJX Companies, and Western Digital. No company met the criteria in seventeen of the twenty-six sectors studied.
IRJET-An Entropy-Weight Based TOPSIS Approach for Supplier SelectionIRJET Journal
This document presents a methodology for supplier selection using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method combined with entropy weight. It involves using a decision matrix to evaluate suppliers based on multiple quantitative and qualitative criteria. The weights of each criterion are determined using an entropy weight calculation. The TOPSIS method is then applied to rank suppliers based on their distance from the ideal positive and negative solutions. An illustrative example is provided to demonstrate the step-by-step calculations for applying this methodology to a supplier selection problem involving four suppliers and five evaluation criteria.
The document describes using the Analytic Hierarchy Process (AHP) to help a couple choose which car to purchase out of three options: Honda, Mazda, or Volvo. It outlines the steps to build an AHP model in Decision Lens software, including establishing criteria of cost, safety, and appearance to evaluate the alternatives. Pairwise comparisons are made between criteria to calculate their weights, and between alternatives for each criterion. The final weights show the Honda as the top choice.
The document describes using the Analytic Hierarchy Process (AHP) in the Decision Lens software to help a couple select the best car to purchase. It walks through building an AHP model in Decision Lens to evaluate three criteria (cost, safety, appearance) and three car alternatives (Honda, Mazda, Volvo). It provides an example of pairwise comparing the criteria, calculating the criteria weights, and discusses entering judgments directly in Decision Lens.
This document provides instructions for how to perform Analytic Hierarchy Process (AHP) analysis in Excel. It discusses developing a hierarchy of decision criteria and alternatives, making pairwise comparisons between elements, normalizing the comparisons to generate weights, and checking consistency. The basic steps are outlined as developing pairwise comparison matrices, normalizing the matrices to obtain weights, and calculating a consistency ratio to validate the judgments.
This document provides an overview of the analytical hierarchy process (AHP), a multiple-criteria decision analysis technique for evaluating project alternatives. AHP involves structuring the decision problem into a hierarchy, measuring priorities through pairwise comparisons, and synthesizing the results. The document includes an example application of AHP and discusses its use in a case study evaluating design options for a grade separation on the California High-Speed Rail project.
The document discusses predicting matches in speed dating using machine learning models. It summarizes the steps taken which include:
1) Cleaning and exploring the speed dating dataset to engineer relevant features like combined interests and age differences.
2) Initial modeling with random forest, logistic regression, SVC and gradient boosting classifiers showed underfitting.
3) Feature selection using random forest importance improved results by focusing on combined interest features.
4) Parameter tuning of random forest and SVC classifiers further optimized performance, with the best models achieving over 95% accuracy.
Hiring- One of the essential aspects of the corporate sector is the most concerning and nerve-wracking area for every organization and to give everyone freedom from stress Gartner Digital Markets comes up with the list of top performing recruitment software every year.
A recruitment software's basic function is to electronically automate the job posting on multiple channels, creating visual hiring pipelines, filtering applications, shortlisting, interview scheduling and helps you in streamlining the recruitment process.
‘Recooty’ – a modern recruitment software company has emerged as a game changer this year, which helps you attract & engage great talents easily.
To create this report, Software Advice evaluated over 320 Applicant Tracking System (ATS). Only those with the top scores for Usability and UserRecommended made the cut as FrontRunners.
Out of all Global Recruitment Software products reviewed by Software Advice in 2019, Recooty scored highest 4.95 out of 5 and emerged the clear recruitment applicant tracking system market leader based on verified user reviews and ratings. Vendors include Recooty, Workable, Bullhorn, Taleo and more.
Econometrics beat dave giles' blog ardl modelling in e_views 9b1mit
This blog post discusses autoregressive distributed lag (ARDL) modelling in EViews 9. Specifically:
1) The post demonstrates how to estimate an ARDL model to examine the relationship between gasoline and crude oil prices in Canada using weekly data from 2000 to 2013.
2) Unit root tests on the logged price series find inconclusive evidence of non-stationarity, making ARDL modelling appropriate.
3) An ARDL model is estimated with lags of the dependent and independent variables as regressors, selected using information criteria.
4) The bounds test allows examination of the long-run relationship between gasoline and crude oil prices.
Why is Sales and Operations Planning So Hard?Lora Cecere
Sales and Operations Planning processes are not a panacea. Just because an organization has a process, does not automatically mean that the company will drive value.
In the past decade, company progress moved backwards with fewer and fewer companies believing that they are successful. The reasons? Lack of definition of supply chain excellence, the need for design, clear delineation of governance, clarity of the role of the financial budget and the organizational tension in reconciliation with the market, and the lack of organizational alignment.
The document discusses various techniques for analyzing brands, including:
1) Understanding industries and competitors to analyze market share, price tiers, and marketing strategies.
2) Using revealed and stated preference data to understand consumer decision-making and brand perceptions.
3) Conducting factor analysis to reduce many brand attributes to a few underlying factors that capture most of the information, which can then be used to position brands on perceptual maps.
This document discusses various techniques for analyzing brands, including understanding industry structure, customer and competitor insights, and different approaches to positioning brands. It covers eliciting brand perceptions through stated preference surveys and revealed preference data. Factor analysis and clustering are presented as methods to understand the underlying dimensions that drive consumer perceptions of brands and group brands accordingly. Perceptual maps can be created by plotting brand locations based on their factor scores to visualize positioning.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
1. BIRLA INSTITUTE OF MANAGEMENT
TECHNOLOGY
MANAGMENT SCIENCE ASSIGNMENT
ANALYTICAL HIERARCHICAL PROCESS
Submitted by: Akshit Gupta Submitted to :
19DM024 Kuldeep Lamba
3. Problem Statement
India is the world’s largesttwo-wheeler market and despite the losses suffered
by bikemakersin Indiaduringthe shiftto BS-IV,theindustryrecoveredwellwith
a massivegrowthof 14.80percent.Overallthe two-wheeler industrysaw a total
sales of over 2.01 cr. units sold in the Indian domestic market and exports from
the country also shotup by over 20 percent. Within the Two Wheelers segment,
Motorcycles grew by 13.69 percent respectively.(Financial Express). From that
report we can conclude that every 5th person owns a 2-wheeler and it is a vast
market. So we try to understand the decision making part which done before
buy a bike. We took five criteria to which helps customers to take decision and
five alternative choice from which customer can choose their final product.
Introduction
AHP
AHP is a decision-making method developed by Prof. Thomas L. Saaty in 1970s
that is used to solvecomplex MCDMproblems. Itrequires a decision maker to
offer judgments about each criterion’s relative importance and specify a
predilection for each decision alternative using each criterion. It is highly
4. efficient in identifying the selected criteria, their weighting, and analysis, and it
allows a logical data combination, which could be quantitative, qualitative,
experience, insight, and intuition in its algorithmic framework. AHP enables
decision makers to determine each criterion’s weight.
Features of AHP
AHP isa very flexibleand powerfultoolbecausethescores, andthereforethe final
ranking,areobtained on thebasisof thepairwiserelative evaluationsof both the
criteriaandtheoptionsprovided bytheuser.ThecomputationsmadebytheAHP
are always guided by the decision maker’s experience, and the AHP can thus be
consideredas a toolthatis ableto translatetheevaluations(both qualitativeand
quantitative) madebythedecisionmakerinto a multicriteria ranking. In addition,
the AHP is simple because there is no need of building a complex expert system
with the decision maker’s knowledge embedded in it
How AHP Works
AHPconsiders a setof evaluation criteria, and a setof alternative options among
which the best decision is to be made. It is important to note that, since some
of the criteria could be contrasting, it is not true in general that the best option
is the one which optimizes each single criterion, rather the one which achieves
the most suitable trade-off among the different criteria.
AHP generates a weight for each evaluation criterion according to the decision
maker’s pairwise comparisons of the criteria. The higher the weight, the more
important the corresponding criterion. Next, for a fixed criterion, the AHP
assigns a score to each option according to the decision maker’s pairwise
comparisons of the options based on that criterion. The higher the score, the
better the performanceof the option with respect to the considered criterion.
Finally, the AHP combines the criteria weights and the options scores, thus
determining a global scoreforeach option, and a consequentranking.Theglobal
scorefor a given option is a weighted sum of the scores itobtained with respect
to all the criteria.
5. Questionnare
After the completion of the structure, the second step of the AHP process is to
make a questionnaire to determine the preference of the respondents. The
questionnaire for a 4 criteria, 3 alternative process would require at least some
questions to be answered in order to make the right decision so as to choose a
preference of car
The questionnaireis madeon the basisof pairwisescaling method, Theselection
criteria is shown the table for reference.
Criteria 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Criteria
Price 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Price 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Price 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Mileage 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Mileage 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Mileage 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Looks 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
Weightage Scale Value
Extremely Important 8, 9
Very Important 6, 7
Moderately Important 4, 5
Important 2, 3
Equally Important 1
Explanation:
6. i. If you choose 1,when comparing Appearance over comfort that means
you give equal importance to both the criteria
ii. If you choose 9 towards the left, It means that Appearance on the left
hand side is 9 times more important than criteria n the right which is
comfort
iii. If you choose 6 towards the right, it means that you give very strong
importance to cost over Appearance
Questions:
Criteria
Questions 1 to 3: what is the relative importance of “PRICE” with respect to
others?
Questions 4 to 5 : whatis the relative importance of “MILEAGE” with respect to
others?
Questions6 : whatis therelative importanceof “LOOKS”with respectto others?
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
PRICE LOOKS
PRICE COLOR
PRICE MILEAGE
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
MILEAGE COLOR
MILEAGE LOOKS
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
LOOKS COLOR
8. Questions 13 to 15 (LOOKS): Relative importance of brands APACHE, ENFIELD,
R15 with respect to LOOKS?
Questions 13 to 15 (COLOR): Relative importance of brands APACHE, ENFIELD,
R15 with respect to COLOR?
Analysis
Consistency Analysis
STEP 1:
The first step of the process is to check for the consistency of the criteria. The
consistency of the data is expected to be within the range of 1% to 10%. Before
this the important step is to answer the questionnaire with utmost sincerity in
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
ENFIELD R15
APACHE R15
ALTERNATIVESCOMPAREDWITHRESPECTTOLOOKS
APACHE ENFIELD
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9
ENFIELD R15
APACHE R15
APACHE ENFIELD
9. order to get a consistent output. The data from the respondent is as shown
below.
STEP 2:
After finding out the summation of all criteria, we normalize the entire matrix
my dividing each value of the column with its sum. Then the weighted average
of each row is also taken. The Sum of each column shall be 1.
STEP 3: The Vales of Lambdamax, Consistency Index(CI), Random Consistency
Index(RI) and the ratio of CI/RI are calculated to find out the consistency of the
data collected.
N=4
From the calculations we can infer that the data is quite consistent and can be
observed that the consistency index is less than 10% as prescribed in the
process. Since the data is consistent, there is no need to repeat the entire
Criteria PRICE MILEAGE LOOKS COLOR
PRICE 1 3.63 5.43 5.24
MILEAGE 0.28 1 0.40 2.00
LOOKS 0.18 2.50 1 4.16
COLOR 0.19 0.50 0.24 1
SUM 1.65 7.63 7 12.4
PairWise Matrix of CRITERIA by synthesing using Geometric Mean
Criteria PRICE MILEAGE LOOKS COLOR Criteria Weights
PRICE 0.61 0.48 0.77 0.42 0.57
MILEAGE 0.17 0.13 0.06 0.16 0.13
LOOKS 0.11 0.33 0.14 0.34 0.23
COLOR 0.12 0.07 0.03 0.08 0.07
4.26
0.0881
0.90
9.79% ACCEPTABLE
Constant
Consistency Ratio (CR)
ƛ max
Consistency Index (CI)
10. process to get the consistency indexless than 10%. Wecan now moveon to the
next step of calculation of alternatives.
Main inference from STEP 3: Price is given the utmost importance by the
respondent whichis placedat 57% of the total share, It is thenfollowedby the
Looks which is given the second importance at 23% followedby Colour, Price
accordingly.
STEP 4 (PRICEto Alternatives): From hereonwards each criterion of selecting a
Bike is rated with respect to all the alternatives which are Apache, Enfield, R15
.All the above three steps are repeated for each criterion. First Criterion of the
Appearance is chosen and compared against all the other brands.
PRICE APACHE ENFIELD R15
APACHE 1 0.24 5.19
ENFIELD 4.16 1 7.56
R15 0.19 0.13 1
Sum 5.35 1.37 13.75
Pairwise Matrix of PRICE by using Geometric Mean
PRICE APACHE ENFIELD R15 Criteria Weights
APACHE 0.19 0.18 0.38 0.25
ENFIELD 0.78 0.73 0.55 0.69
R15 0.04 0.10 0.07 0.07
Normalised Matrix
Criteria Weights 0.25 0.69 0.07
PRICE APACHE ENFIELD R15 Sum Sum/Weight
APACHE 0.25 0.16 0.35 0.77 3.11
ENFIELD 1.03 0.69 0.52 2.23 3.25
R15 0.05 0.09 0.07 0.21 3.02
Weighted Normalised Matrix
11. Main inference: Price wise ENFIELD is given the utmost importance by then
followed by the APACHE which is given the second importance followed by
R15 accordingly.
STEP 5 (MILEAGE to Alternatives): Second Criterion of the Comfort is chosen
and compared against all the other brands alternatives APACHE, ENFIELD, R15
Main inference: MILEAGE wise R15 is given the utmost importance by then
followed by the APACHE which is given the second importance followed by
ENFIELD accordingly.
STEP 6 (LOOKS to Alternatives): Second Criterion of the Comfort is chosen and
compared against all the other brands alternatives APACHE, ENFIELD, R15.
MILEAGE APACHE ENFIELD R15
APACHE 1 3.30 0.23
ENFIELD 0.30 1 0.17
R15 4.35 5.77 1
Sum 5.65 10.07 1.40
MILEAGE APACHE ENFIELD R15 Criteria Weights
APACHE 0.18 0.33 0.16 0.22
ENFIELD 0.05 0.10 0.12 0.09
R15 0.77 0.57 0.71 0.69
Criteria Weights 0.22 0.09 0.69
MILEAGE APACHE ENFIELD R15 Sum Sum/Weight
APACHE 0.22 0.30 0.16 0.68 3.06
ENFIELD 0.07 0.09 0.12 0.28 3.01
R15 0.97 0.53 0.69 2.18 3.18
3.09
0.0431
0.58
7.43% ACCEPTABLE
Pairwise Matrix of MILEAGE by using Geometric Mean
Normalised Matrix
Weighted Normalised Matrix
ƛ max
Consistency Index (CI)
Constant
Consistency Ratio (CR)
3.13
0.0632
0.90
7.03% ACCEPTABLE
ƛ max
Consistency Index (CI)
Constant
Consistency Ratio (CR)
12. Main inference:LOOKS wise APACHE is given the utmost importance by then
followed by the ENFIELD which is given the second importance followed by
R15 accordingly.
STEP 7 (COLOR to Alternatives): Second Criterion of the Comfort is chosen and
compared against all the other brands alternatives APACHE, ENFIELD, R15.
LOOKS APACHE ENFIELD R15
APACHE 1 2.71 6.46
ENFIELD 0.37 1 5.77
R15 0.15 0.17 1
SUM 1.52 3.89 13.23
LOOKS APACHE ENFIELD R15 Criteria Weights
APACHE 0.66 0.70 0.49 0.61
ENFIELD 0.24 0.26 0.44 0.31
R15 0.10 0.04 0.08 0.07
Criteria Weights 0.61 0.31 0.07
LOOKS APACHE ENFIELD R15 Sum Sum/Weight
APACHE 0.61 0.85 0.48 1.94 3.15
ENFIELD 0.23 0.31 0.43 0.96 3.09
R15 0.10 0.05 0.07 0.22 3.02
3.09
0.0443
0.58
7.64% ACCEPTABLE
Pairwise Matrix of LOOKS by using Geometric Mean
Normalised Matrix
Weighted Normalised Matrix
ƛ max
Consistency Index (CI)
Constant
Consistency Ratio (CR)
13. Maininference:COLORS wise ENFIELDis giventhe utmostimportance by then
followed by the APACHE which is given the second importance followed by
R15 accordingly.
STEP 8: In the last and final step to find out the global weighted average we
multiply the weighted average of each criteria against the weighted average
given to each criteria of each brand. Once each of these are multiplied the final
preference of the respondents can be derived using the table.
Main inference: It is observed from the survey that ENFIELD is the most
preferred bike with criteria such as Price, Mileage, Color, Looks. The
observations show that 53% of the respondents have chosen over the other
COLOR APACHE ENFIELD R15
APACHE 1 0.14 2.62
ENFIELD 6.95 1 6.95
R15 0.38 0.14 1
Sum 8.33 1.29 10.57
COLOR APACHE ENFIELD R15 Criteria Weights
APACHE 0.12 0.11 0.25 0.16
ENFIELD 0.83 0.78 0.66 0.76
R15 0.05 0.11 0.09 0.08
Criteria Weights 0.16 0.76 0.08
Age APACHE ENFIELD R15 Sum Sum/Weight
APACHE 0.16 0.11 0.22 0.49 3.06
ENFIELD 1.11 0.76 0.58 2.45 3.24
R15 0.06 0.11 0.08 0.25 3.02
3.11
0.0534
0.58
9.21%
Pairwise Matrix of COLOR by using Geometric Mean
Normalised Matrix
Weighted Normalised Matrix
ƛ max
Consistency Index (CI)
Constant
Consistency Ratio (CR)
Criteria Weights APACHE ENFIELD R15 APACHE ENFIELD R15
PRICE 0.57 0.25 0.69 0.07 0.14 0.39 0.04
MILEAGE 0.13 0.22 0.09 0.69 0.03 0.01 0.09
LOOKS 0.23 0.61 0.31 0.07 0.14 0.07 0.02
COLOR 0.07 0.16 0.76 0.08 0.01 0.06 0.01
0.32 0.53 0.15SUM
ORIGINAL SCORE WEIGHTED SCORE
14. brands, while the other two brands i.e. APACHE and R15 are very low
compared to the choice of Enfield.
Summary
The following table shows the ranking order of each criteria against along each
alternative for a better understanding and interpretation of the report.
In nutshell, the most preferred Bike among all criteria and among all the
alternatives, Enfield is the clear winner and is the most preferred bike brand by
the chosen 3 respondents.
References
Introduction to management science. Sweeney, Anderson
Wikipedia
https://www.pmi.org/learning
https://cran.r-project.org/web/packages/ahp/
Criteria Weights APACHE ENFIELD R15 APACHE ENFIELD R15
PRICE 0.57 0.25 0.69 0.07 0.14 0.39 0.04
MILEAGE 0.13 0.22 0.09 0.69 0.03 0.01 0.09
LOOKS 0.23 0.61 0.31 0.07 0.14 0.07 0.02
COLOR 0.07 0.16 0.76 0.08 0.01 0.06 0.01
0.32 0.53 0.15
RANK 2 1 3
SUM
ORIGINAL SCORE WEIGHTED SCORE