This document discusses decision making and optimization problems faced by managers and consumers. It explains that decision making involves finding the best solutions under given circumstances. Managers aim to maximize profit or minimize costs, while consumers aim to maximize satisfaction given constraints like prices and income. The document also discusses concepts like objective functions, choice variables, constrained vs unconstrained optimization, and marginal analysis, which are analytical tools used to solve optimization problems.
The document discusses various sampling techniques used in survey research. It defines population, sample, census, and sampling. Probability and non-probability sampling methods are described. Probability methods ensure each unit has a known chance of selection and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, area sampling, and multistage sampling. Non-probability methods rely on availability or human judgment and include accidental, convenience, judgment, purposive, and quota sampling. Advantages and limitations of different techniques are also provided.
The document discusses simple linear regression analysis. It provides definitions and formulas for simple linear regression, including that the regression equation is y = a + bx. An example is shown of using the stepwise method to determine if there is a significant relationship between number of absences (x) and grades (y) for students. The analysis finds a significant negative relationship, meaning more absences correlated with lower grades. The document also discusses using the regression equation to predict outcomes and the significance test for the slope of the regression line.
Sampling technique for 2 nd yr pbbsc nsgsindhujojo
This document provides information on sampling terminology and methods. It defines key terms like population, sample, sampling frame, and element. It also describes various probability and non-probability sampling techniques. Specifically, it covers simple random sampling in detail, including how to select a simple random sample using lottery method, random number tables, or a computer. It explains the difference between sampling with and without replacement. Finally, it briefly discusses stratified sampling and how it involves dividing a population into homogeneous subgroups or strata before sampling.
1. The document outlines the process of estimating demand functions using statistical techniques, including identifying variables, collecting data, specifying models, and estimating parameters.
2. Linear and nonlinear models are discussed for relating dependent and independent variables, with the linear model being most common. Estimating techniques include ordinary least squares regression.
3. Regression results can be used to interpret relationships between variables and make predictions, though correlation does not necessarily imply causation. Testing procedures evaluate the model fit and significance of relationships.
Review of "Survey Research Methods & Design in Psychology"James Neill
This document provides an overview and summary of a lecture on survey research and design in psychology. It discusses key topics like the research process, survey design, data analysis, reliability and validity, sampling, and reporting results. Assessment involves a lab report on survey-based research and a final exam testing knowledge of research methods and statistical analysis techniques.
The document provides an overview of regression analysis concepts including:
- Regression analysis is used to understand relationships between variables and predict the value of one variable based on another.
- A regression model has a dependent variable on the y-axis and an independent variable on the x-axis.
- Examples of how to perform regression analysis are provided including creating a scatter plot and calculating parameters like the slope and intercept.
- Key concepts for measuring the fit of a linear regression model are defined including variability, correlation coefficient, coefficient of determination, and standard error.
This document discusses various aspects of modern marketing approaches, including social marketing, relationship marketing, and green marketing. Social marketing utilizes social media and word-of-mouth recommendations. Relationship marketing focuses on retaining existing customers through loyalty programs and ongoing customer service. Green marketing promotes environmentally-friendly products and sustainable business practices. Many large companies are investing heavily in these areas to both appeal to consumers and address environmental concerns.
The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.
The document discusses various sampling techniques used in survey research. It defines population, sample, census, and sampling. Probability and non-probability sampling methods are described. Probability methods ensure each unit has a known chance of selection and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, area sampling, and multistage sampling. Non-probability methods rely on availability or human judgment and include accidental, convenience, judgment, purposive, and quota sampling. Advantages and limitations of different techniques are also provided.
The document discusses simple linear regression analysis. It provides definitions and formulas for simple linear regression, including that the regression equation is y = a + bx. An example is shown of using the stepwise method to determine if there is a significant relationship between number of absences (x) and grades (y) for students. The analysis finds a significant negative relationship, meaning more absences correlated with lower grades. The document also discusses using the regression equation to predict outcomes and the significance test for the slope of the regression line.
Sampling technique for 2 nd yr pbbsc nsgsindhujojo
This document provides information on sampling terminology and methods. It defines key terms like population, sample, sampling frame, and element. It also describes various probability and non-probability sampling techniques. Specifically, it covers simple random sampling in detail, including how to select a simple random sample using lottery method, random number tables, or a computer. It explains the difference between sampling with and without replacement. Finally, it briefly discusses stratified sampling and how it involves dividing a population into homogeneous subgroups or strata before sampling.
1. The document outlines the process of estimating demand functions using statistical techniques, including identifying variables, collecting data, specifying models, and estimating parameters.
2. Linear and nonlinear models are discussed for relating dependent and independent variables, with the linear model being most common. Estimating techniques include ordinary least squares regression.
3. Regression results can be used to interpret relationships between variables and make predictions, though correlation does not necessarily imply causation. Testing procedures evaluate the model fit and significance of relationships.
Review of "Survey Research Methods & Design in Psychology"James Neill
This document provides an overview and summary of a lecture on survey research and design in psychology. It discusses key topics like the research process, survey design, data analysis, reliability and validity, sampling, and reporting results. Assessment involves a lab report on survey-based research and a final exam testing knowledge of research methods and statistical analysis techniques.
The document provides an overview of regression analysis concepts including:
- Regression analysis is used to understand relationships between variables and predict the value of one variable based on another.
- A regression model has a dependent variable on the y-axis and an independent variable on the x-axis.
- Examples of how to perform regression analysis are provided including creating a scatter plot and calculating parameters like the slope and intercept.
- Key concepts for measuring the fit of a linear regression model are defined including variability, correlation coefficient, coefficient of determination, and standard error.
This document discusses various aspects of modern marketing approaches, including social marketing, relationship marketing, and green marketing. Social marketing utilizes social media and word-of-mouth recommendations. Relationship marketing focuses on retaining existing customers through loyalty programs and ongoing customer service. Green marketing promotes environmentally-friendly products and sustainable business practices. Many large companies are investing heavily in these areas to both appeal to consumers and address environmental concerns.
The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.
The document shows employment figures for India's public and private sectors from 1970-71 to 2007-08. It indicates that:
1) Employment in the public sector steadily increased from 11.1 million in 1970-71 to 17.67 million in 2007-08.
2) Employment in the private sector increased more slowly from 6.73 million in 1970-71 to 9.84 million in 2007-08.
3) The number of persons on the live register, a measure of unemployment, fluctuated between 5.1 million and 42 million from 1970-71 to 2007-08.
1) Agricultural risks in Sub-Saharan Africa are linked to low soil fertility, unpredictable water availability due to climate, and low use of soil amendments and other inputs.
2) These risks have contributed to low and unstable crop yields, increasing poverty and hunger in the region.
3) Risk mitigation strategies are needed to address soil fertility depletion and make better use of water resources through improved farming practices and investment in soil health and water management.
André Bationo - Agricultural risks linked to soil, water and climate in Sub-S...Global Risk Forum GRFDavos
Agricultural risks in Sub-Saharan Africa are linked to soil, water, and climate. Risks stem from inherently low soil fertility and low use of inputs like fertilizers. Water and climate risks include high rainfall variability, recurrent droughts and floods, which reduce agricultural productivity and economic growth. Efforts to mitigate risks include soil and water conservation techniques, use of organic and mineral fertilizers, development and dissemination of improved seeds, and other sustainable land management practices. However, widespread adoption of risk-reducing technologies remains low.
This document contains attendance data for 100 sessions with totals. It shows:
- The total attendance was 72 people out of a total census population of N
- Attendance ranged from 66 to 52 people for most sessions
- Attendance increased to 70 or more for some later sessions
The file with the highly informative name "other data"mkalina
The document contains data on China's economy from 1952-1976 including:
- Exports, imports and trade balance from 1957-1974 with exports and imports generally increasing over time.
- Agricultural and industrial production from 1952-1972 showing increases in items like grain, steel, oil, power.
- Mechanization and irrigation of agriculture expanding from 1952-1970.
- Price changes, money supply, incomes and wages fluctuating from 1953-1976 with periods of growth and decline.
The document appears to be a listing of company stock information from the NYSE MKT stock exchange, including the company name, current and 52-week high/low stock prices, dividend yield if any, last closing price and change from the previous day, and year-to-date percentage change. Over 100 company listings are provided in a table format with company details.
The document discusses challenges that companies in Manila face in recruiting qualified applicants despite an abundant labor supply. Specifically, 39.3% of respondents identified finding qualified applicants as a major issue. Additionally, low productivity and lack of work ethics were among the top HR concerns. The document proposes that VLearning, an e-training platform customized for each company, can help address these concerns by reducing costs, conducting trainings anywhere, modernizing methods, and improving collaboration, skills, and productivity.
Mac OS X Snow Leopard introduces Stacks, which allow users to view and access files and applications directly from the Dock. Stacks automatically organize their contents in a fan or grid based on item number, and users can customize sort order and view style. Common file folders like Documents, Downloads, and Applications are set up as stacks by default for convenient access to recent items without opening additional windows.
The document discusses optimal risky portfolios and diversification. It covers how portfolio risk depends on the correlation between asset returns, how to calculate portfolio variance and risk for two and three asset portfolios, the benefits of diversification in reducing risk, and how to construct efficient portfolios using the Markowitz model. The key benefits of diversification are reducing non-systematic risk and obtaining portfolios on the efficient frontier with higher risk-adjusted returns.
This document is a chapter about learning about return and risk from analyzing historical data. It discusses factors that influence interest rates and defines real and nominal rates. It explains how the equilibrium real rate of interest is determined and shows the relationship between nominal interest rates and expected inflation. The chapter also covers topics like taxes and interest rates, comparing rates of return over different holding periods, and defining expected returns and standard deviation. It provides examples of analyzing the historical record of returns on Treasury bills, stocks, and other asset classes.
This document outlines VLearning's marketing plan to target students, employees, teachers, schools, and employers as potential users of its virtual learning platform. It will pursue both B2B and B2C sales strategies. The platform is positioned as a user-friendly virtual learning tool for content sharing, classroom extension, training, and collaboration. Next steps include identifying prospective customer accounts, developing a detailed launch plan, and building a sales team.
This document outlines VLearning's marketing plan to target students, employees, teachers, schools, and employers as potential users of its virtual learning platform. It will pursue both B2B and B2C sales strategies. Key features of the platform include being user-friendly, flexible, and allowing easy content sharing similar to social media sites. The marketing challenges are to build awareness of the platform and educate the market on the value of augmenting physical classrooms with virtual learning. The positioning is that VLearning is the only such platform in the country that provides a familiar social media-like experience for both users and content creators. The next steps outlined are to identify prospective accounts, develop a detailed one quarter marketing launch plan, and build a
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
The document shows employment figures for India's public and private sectors from 1970-71 to 2007-08. It indicates that:
1) Employment in the public sector steadily increased from 11.1 million in 1970-71 to 17.67 million in 2007-08.
2) Employment in the private sector increased more slowly from 6.73 million in 1970-71 to 9.84 million in 2007-08.
3) The number of persons on the live register, a measure of unemployment, fluctuated between 5.1 million and 42 million from 1970-71 to 2007-08.
1) Agricultural risks in Sub-Saharan Africa are linked to low soil fertility, unpredictable water availability due to climate, and low use of soil amendments and other inputs.
2) These risks have contributed to low and unstable crop yields, increasing poverty and hunger in the region.
3) Risk mitigation strategies are needed to address soil fertility depletion and make better use of water resources through improved farming practices and investment in soil health and water management.
André Bationo - Agricultural risks linked to soil, water and climate in Sub-S...Global Risk Forum GRFDavos
Agricultural risks in Sub-Saharan Africa are linked to soil, water, and climate. Risks stem from inherently low soil fertility and low use of inputs like fertilizers. Water and climate risks include high rainfall variability, recurrent droughts and floods, which reduce agricultural productivity and economic growth. Efforts to mitigate risks include soil and water conservation techniques, use of organic and mineral fertilizers, development and dissemination of improved seeds, and other sustainable land management practices. However, widespread adoption of risk-reducing technologies remains low.
This document contains attendance data for 100 sessions with totals. It shows:
- The total attendance was 72 people out of a total census population of N
- Attendance ranged from 66 to 52 people for most sessions
- Attendance increased to 70 or more for some later sessions
The file with the highly informative name "other data"mkalina
The document contains data on China's economy from 1952-1976 including:
- Exports, imports and trade balance from 1957-1974 with exports and imports generally increasing over time.
- Agricultural and industrial production from 1952-1972 showing increases in items like grain, steel, oil, power.
- Mechanization and irrigation of agriculture expanding from 1952-1970.
- Price changes, money supply, incomes and wages fluctuating from 1953-1976 with periods of growth and decline.
The document appears to be a listing of company stock information from the NYSE MKT stock exchange, including the company name, current and 52-week high/low stock prices, dividend yield if any, last closing price and change from the previous day, and year-to-date percentage change. Over 100 company listings are provided in a table format with company details.
The document discusses challenges that companies in Manila face in recruiting qualified applicants despite an abundant labor supply. Specifically, 39.3% of respondents identified finding qualified applicants as a major issue. Additionally, low productivity and lack of work ethics were among the top HR concerns. The document proposes that VLearning, an e-training platform customized for each company, can help address these concerns by reducing costs, conducting trainings anywhere, modernizing methods, and improving collaboration, skills, and productivity.
Mac OS X Snow Leopard introduces Stacks, which allow users to view and access files and applications directly from the Dock. Stacks automatically organize their contents in a fan or grid based on item number, and users can customize sort order and view style. Common file folders like Documents, Downloads, and Applications are set up as stacks by default for convenient access to recent items without opening additional windows.
The document discusses optimal risky portfolios and diversification. It covers how portfolio risk depends on the correlation between asset returns, how to calculate portfolio variance and risk for two and three asset portfolios, the benefits of diversification in reducing risk, and how to construct efficient portfolios using the Markowitz model. The key benefits of diversification are reducing non-systematic risk and obtaining portfolios on the efficient frontier with higher risk-adjusted returns.
This document is a chapter about learning about return and risk from analyzing historical data. It discusses factors that influence interest rates and defines real and nominal rates. It explains how the equilibrium real rate of interest is determined and shows the relationship between nominal interest rates and expected inflation. The chapter also covers topics like taxes and interest rates, comparing rates of return over different holding periods, and defining expected returns and standard deviation. It provides examples of analyzing the historical record of returns on Treasury bills, stocks, and other asset classes.
This document outlines VLearning's marketing plan to target students, employees, teachers, schools, and employers as potential users of its virtual learning platform. It will pursue both B2B and B2C sales strategies. The platform is positioned as a user-friendly virtual learning tool for content sharing, classroom extension, training, and collaboration. Next steps include identifying prospective customer accounts, developing a detailed launch plan, and building a sales team.
This document outlines VLearning's marketing plan to target students, employees, teachers, schools, and employers as potential users of its virtual learning platform. It will pursue both B2B and B2C sales strategies. Key features of the platform include being user-friendly, flexible, and allowing easy content sharing similar to social media sites. The marketing challenges are to build awareness of the platform and educate the market on the value of augmenting physical classrooms with virtual learning. The positioning is that VLearning is the only such platform in the country that provides a familiar social media-like experience for both users and content creators. The next steps outlined are to identify prospective accounts, develop a detailed one quarter marketing launch plan, and build a
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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Report man econ
1.
2. Decisionmaking is a process by
which "best solutions‖ are
found.
Managers are the one who make
the decisions to lead to the best
outcome possible under a given
circumstances.
3. Itis an act, process or
methodology of making
something fully perfect,
functional or as effective as
possible.
4. Case of a MANAGER
- A manager always find the level
of output that maximizes the
profit of the firm or to determine
how much labor, capital and raw
material inputs to use to produce
a given amount of output at the
lowest possible cost.
5. Case of Consumers
- As consumers, they will search goods
within the constraints imposed by
their prices and their income, for the
combination of goods and services that
will yield the highest level of
satisfaction.
6. The function the decision maker
seeks to maximize or minimize
Examples:
1. Manager – will always try to
maximize profit.
2. Consumer – will always
maximize consumer goods.
7. - Optimization problem that involves
maximizing/minimizing the objective
function.
8. - When the Objective function
measures a benefit, the decision
maker seeks to maximize this benefit
thus solving a maximization problem.
- When the Objective function
measures a cost, the decision maker
seeks to minimize the cost, thus
solving a minimizing problem.
9. Determines the objective function.
Example:
- Profit
The value of profit will be determined by the
number of units sold or produced while the
production of unit of the good is the activity or
choice variable that determines the value of the
objective function which is profit.
10.
11. ObjectiveFunction- Measures whatever it is
that the particular decision maker wishes to
either maximize or minimize.
E.g. profit, cost, satisfaction…
Maximization Problem- optimizing problem
that involves maximizing the objective
function
12. DiscreteChoice Variable- choice Variable
that can only take a specific integer
ContiniousChoice Variable- choice variable
that can take on any value between two end
point.
13. Minimization Problem- optimizing problem
that involves minimizing the objective
function
Activities
or Choice variables- Determine the
value of Objective function.
Objectivefunction maybe a function of more
than one activity
14. Unconstrained optimization- an optimization
problem wherein the decision maker can
choose any level of activity from unrestricted
set of values.
E.g.no external restrictions inchoosing any
level of output in order to maximize net
benefit.
15. Constrained Optimization- Optimization
problems wherein the decision maker can
choose values for choice variables from a
restricted set of values
Constrained maximization- maximization
problem where activities must be chose to
satisfy a side constraint that the total cost of
activities be held to specific amount
Total benefit function=objective function
Total cost= constraint
16. Constrained Minimization- minimization
problem where the activities must be chosen
to satisfy a side constraint that the total
benefit of the activities be held to specific
amount.
Objective function= Total cost function
Total benefit function= constraint
E.g. gift shop
17. MarginalAnalysis – analytical tool for solving
optimization problems that involves changing
the value of choice variable by a small
amount to see if the objective function can
be further increased or further decreased
18. Unconstrained Maximization
NB= TB-TC
NB=net benefit
TB=total benefit
TC=total cost
The activity is increased or decreased in
order to obtain highest level of benefit
The optimal level of activity is obtained
when no further increase in net benefit are
possible in any change of activity
19. MarginalBenefit- addition to total benefit
attribute to increasing the activity by a small
amount.
MarginalCost- addition to total cost attribute
to increasing the activity by a small amount
20. MB= Change in total benefit
Change in activity
MC= Change in total cost
Change in activity
MB>MC MB<MC
Increase activity NB rises NB falls
Decrease activity NB falls NB rises
Optimal level of the activity is attained-net benefit is
maximized-when level of activity is the last level for which
marginal benefit exceeds marginal cost
21. Maximization with a Continuous Choice variable
When a decision maker wishes to obtain the
maximum net benefit from an activity that is
continuously variable, the optimal level of the
activity is that level at which the marginal
benefit is equal to marginal cost (MB=MC)
22. Constrained Optimization
- An objective function is maximized or minimized
subject to a constraint if, for all of the activities
in the objective function, the ratios of marginal
benefit per dollar spent be equal for all
activites.
- MBA/PA=MBB/PB
23.
24. Thischapter set forth the basic principles of
regression analysis: estimation and
assessment of statistical significance. We
emphasized how to interpret the results of
regression analysis, rather than focusing on
the mathematics of regression analysis.
25. The coefficients in an equation that
determine the exact mathematical relation
among variables.
Y being the dependent variable and X the
independent or the explanatory variable.
26. Itis the process of finding estimates of the
numerical values of the parameters of
equation.
27. Thetwo variable linear model or the simple
regression analyisis is used for testing
hypothesis using the Y variable or the
independent variable and X variable or the
explanatory varible.
35. b1 (slope of the estimated = XiYi / Xi2
regression line) = 1.66
b0 (Y intercept) = 27.13 = mean of Yi - (bi * mean of
Ŷi (estimated Regression Xi )
equation) = 27.12 + 1.66Xi
37. Coeffi Standar t Stat P- Lowe Upper Lower Upper
cients d Error value r 95% 95% 95.0% 95.0%
Intercept 27.13 1.9792653 13.70457 7.74557E 22.5608 31.68919 22.56080 31.689194
48 984 -07 0593 407 593 07
X 1.66 0.1013210 16.38081 1.94353E 1.42607 1.893369 1.426075 1.8933690
87 745 -07 5378 067 378 67
Variable
1
38. Regression Statistics
Multiple R 0.985418303
R Square 0.971049232
Adjusted R Square 0.967430386
Standard Error 2.431706077
Observations 10
39. ANOVA
d SS MS F Significance F
f
Regression 1 1586.694444 1586.694444 268.3311803 1.94353E-07
Residual 8 47.30555556 5.913194444
Total 9 1634
40.
41.
42.
43.
44. Tcomp is greater than the Tcrit (16.38081745>1.860).
Since that is the case then X variable is significant
with the margin of error given, which is 5%, to
explicate the relationship between X and Y
parameters.
• R2 or the explanatory power of the model is equal
to 0.9710 or 97.10%. This explains that fertilizer (X)
expresses 97.10% of output change in Corn . The R2 is
significantly different from zero.
• In F distribution, the Fcomp explains that the
parameters are not all equal to zero. The high value
of F ratio implies a significant relationship between
the dependent and independent variables.
45. The test of significance of parameter
estimates passed as well as the test for the
coefficient of multiple determination and
test of the overall significance of the
regression.
46. Population Regression Line Sample Regression Line
The equation or line The line that best fits
representing the the data in the
true or (actual) sample is call the
relation between sample regression line
dependent variable
and the explanatory
Variable
47. Anestimator that produces estimates of a
parameter that are on average equal to the
true value of the parameter
48. Thedistribution (and relative frequency) of
values b can take because observations on Y
and X come from a random sample
49. The estimated coefficient is far enough away
from zero
Either sufficiently greater than zero (a
positive estimate) or sufficiently less than
zero (a negative estimate)
50. t-stat is used to test the hypothesis that the
true value of b equals zero
If the t-stat is greater than the critical value
of t, then the hypothesis that b=0 is rejected
in favor of the alternative hypothesis that
b not =0
When the calculated t-stat exceeds the
critical value of t, b is significantly different
from zero, or equivalently, b is statistically
significant
51. Using P-value
We use P-value for analyzing data and to make the strongest possible
conclusion from the limited data’s that are given.
To get the p-value, you need to have the estimated value then the
significance level of the alternative hypothesis then the test statistics.
Decision Criterion for a Hypothesis Test Using the P-value:
If P-value is less than a, reject the null hypothesis; otherwise, fail to reject the null hypothesis.
Examples:
Ha: µ 30 versus Ho: µ = 30
Assumptions: X is normally distributed with s = 8
Test Statistic:
a = .05 RR: z < -1.96 or z >1.96 (P-value < .05)
Calculation: z = 1.54
P-value = 2P(z > |zcalculated|) = 2P(z > |1.54|) = 2P(z < -1.54)
= 2(.0618) = .1236
Decision: Fail to reject Ho.
52. Evaluation of Regression Equation
Regression Equation(y) = a + bx
Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2)
Intercept(a) = (ΣY - b(ΣX)) / N
where
x and y are the variables.
b = The slope of the regression line
a = The intercept point of the regression line and the y axis.
N = Number of values or elements
X = First Score
Y = Second Score
ΣXY = Sum of the product of first and Second Scores
ΣX = Sum of First Scores
ΣY = Sum of Second Scores
ΣX2 = Sum of square First Scores
Regression Example: To find the Simple/Linear Regression of
X Values 60 61 66 63 65
Y Values 3.1 3.2 3.8 4 4.1
To find regression equation, we will first find slope, intercept and use it to form regression
equation..
Step 1: Count the number of values.
N=5
Step 2: Find XY, X2
53. Step 3: Find ΣX, ΣY, ΣXY, ΣX2.
ΣX = 311
ΣY = 18.6
ΣXY = 1159.7
ΣX2 = 19359
Step 4: Substitute in the above slope formula given.
Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2)
= ((5)*(1159.7)-(311)*(18.6))/((5)*(19359)-(311)2)
= (5798.5 - 5784.6)/(96795 - 96721)
= 13.9/74
= 0.19
Step 5: Now, again substitute in the above intercept formula given.
Intercept(a) = (ΣY - b(ΣX)) / N
= (18.6 - 0.19(311))/5
= (18.6 - 59.09)/5
= -40.49/5
= -8.098
Step 6: Then substitute these values in regression equation formula
Regression Equation(y) = a + bx
= -8.098 + 0.19x.
Suppose if we want to know the approximate y value for the variable x = 64. Then we can
substitute the value in the above equation.
Regression Equation(y) = a + bx
= -8.098 + 0.19(64).
= -8.098 + 12.16
= 4.06
54. Coefficient of determination
It is used for statistical models whose main purpose is to predict the outcome of the
future based by other related information.
Measures percentage variation in Y that can be explained by the X’s through the model
Y=Xβ + ε
Proportionate reduction of total variation in Y associated with the use of the set of
independent variables X1, X2, …, Xk (assuming a constant term is included in the
model)
A goodness of fit measure
Consider Tampa sales example. From printout, R2 = 0.9453.
• Interpretation: 94% of the variability observed in sale prices can be
explained by assessed values of homes. Thus, the assessed value of the
home contributes a lot of information about the home’s sale price.
• We can also find the pieces we need to compute R2 by hand in either
JMP or SAS outputs:
– SSyy is called Sum of Squares of Model in SAS and JMP
SSE is called Sum of Squares of Error in both SAS and JMP.
• In Tampa sales example, SSyy = 1673142, SSE = 96746 and thus
R2 = 1673142 − 96746/1673142 = 0.94.
55. F-test
F-test is a simultaneous test that if all of the beta=0 (it means that all of
your x’s are useless) or at least one x is not equal to zero (which means
that specific variable is affecting Y).
Ex:. The hypothesis that the means of several normally distributed
populations, all having the same standard deviation, are equal. This is
perhaps the best-known F-test, and plays an important role in the
analysis of variance (ANOVA).
The hypothesis that a data set in a regression analysis follows the
simpler of two proposed linear models that are nested within each other.
56. Multiple Regression
It’s purpose is to learn more about the relationship between
several independent and dependent variables.
Ex. A car agent having a listing of the following cars in the
following characteristics of that car—transportation, comfort,
style, luxury, fuel economy, etc. Once these following
information has been compiled for the various cars, it will be
interesting to see whether and how these measures relate to
the price for which a car is sold. For example, the space of this
car is better analyst of the price of which a car sells than how
luxurious the car is.
58. Are used when the underlying relation between
X and Y plots as a curve, rather than a
straight line.
59.
60.
61. An analyst would use nonlinear regression
model when the scatter diagram shows a
curvilinear pattern.
Nonlinear regression is a general technique
to fit a curve through your data.
The purpose of linear regression is to find the
line that comes closest to your data.
63. One of the most useful nonlinear forms for
managerial economics
expressed as Y = a + bX + cX2
Nonlinear model to Linear model
Create a new variable. ―Z‖ defined as Z = X2
Y= a + bX + cZ
64. Run Regression of Y from X and Z
Dependent Variable: Y F-Ratio: 13.11
R-
Y X Z Observations: 12 squared: 0.75
82 3 9
107 3 9 Parameter Standard T-
61 4 16 Variable Intercept Error Ratio
77 5 25
68 6 36 Intercept 140 17.14 8.17
30 8 64 X -20 4.14 -4.83
57 10 100 Z 1.01 0.5 2
40 12 144
82 14 196
68 15 225
102 17 289
110 18 324
65. Estimated quadratic regression equation is
Y = 140.08 – 19.51X + 1.01X2
1.01 is also the slope parameter estimate for
X2
The estimated equation can be used to
estimate the value of Y for any particular
value of X
66. Example: if X = 10
Y will be equal to 45.98
Y=140.08 – 19.51(10) + 1.01(10)2
After which, perform a t-tests to determine
the statistical significance of each
parameters.
67. Y is related to one or more explanatory variables
in a multiplicative form
Y=aXb Zc
Transform to a linear equation
Percentage change in Y
• b
Percentage change in X
Percentage change in Y
• c
Percentage change in Z
68. Parameters b and c are elasticities.
To transform the equation in to a
linear form, we must use the natural
logarithms of both sides of the
equation.
lnY = (ln a) + b(ln X) + c(ln Z)
If we define: Y’ = a’ + bX’ + cZ’
69. Example
Variable: Y = aXb
Since Y is positive at all points, parameter a is expected to be positive.
Since Y is decreasing as X increases, the parameter X(b) is expected to be negative.
Y X
2810 20
2825 30
2031 30
2228 40
1620 40
1836 50
1217 60
1110 90
1000 110
420 120
602 140
331 170
70. To estimate the parameters a and b in a nonlinear
equation, we transform the equation by taking
logarithms:
lnY = ln a + b lnX
LOG Y LOG X
7.94094 2.99573
7.94626 3.4012
7.61628 3.4012
7.70886 3.68888
7.39018 3.68888
7.51534 3.91202
7.10414 4.09434
7.01212 4.49981
6.90776 4.70048
6.04025 4.78749
6.40026 4.94164
5.80212 5.1358
71. Run Regression
Dependent
Variable LOG Y F-Ratio 70
Observations 12 R-Square 0.875
Parameter Standard T-
Variable Intercept Error Ratio
Intercept 11.06 0.48 23.04
Log X -0.96 0.11 -8.73
72. To obtain parameter estimates:
note: the slope parameter on Log X is also the exponent on X in the
linear equation.
Y=aX(-0.96)
note: since b is an elasticity, 10 percent increase in X results in a 9.6
percent decrease Y.
To obtain the estimate of a:
note: we take the antilog of the estimated value of the intercept
parameter.
= antilog(11.06)
= 63, 576
Y=63,576X(-0.96)
73. Showthat the two models are
mathematically equivalent.
logX = 4.5
logY = 6.74 = [11.06 – 0.96(4.5)]
take the antilog of Y and X.
X = 90 Y = 845
Y = 845 = [63,577(90) (-0.96) ]
74. Regression analysis is simply a tool to provide
the necessary information for a manager to
make decisions that maximizes profits.
Itoffers managers a way of estimating the
functions they need for managerial decision
making