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Mathematical modeling is a process that uses mathematical concepts and language to describe and understand real-world phenomena. This involves formulating hypotheses about the relationships and rates of change between variables, which are then expressed through differential equations. Once a mathematical model is developed, the problem becomes solving these equations, which can be analyzed through various modeling methods to predict future behavior and understand the underlying processes.

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Dams and flash floods

This teaching PPT was used to introduce the physics behind dams and the consequences of the Johnstown, PA flood.

Chapter 1

Mathematical modeling is a process that uses mathematical concepts and language to describe and understand real-world phenomena. This involves formulating hypotheses about the relationships and rates of change between variables, which are then expressed through differential equations. Once a mathematical model is developed, the problem becomes solving these equations, which can be analyzed through various modeling methods to predict future behavior and understand the underlying processes.

P90 x fitness guide

P90X presents a non-linear periodization model consisting of 6 phases that cycle through growth, recovery, and adaptive intensity periods to drive continuous muscle growth and strength gains. The program uses bodyweight and dynamic exercises as well as yoga and martial arts moves to build strength, power, flexibility, and cardiovascular fitness through constantly challenging movements.

War of 1812

The document discusses several activities for students to learn about the War of 1812. Students are asked to discuss in pairs and write responses about impressment and how people might react today. They also investigate the causes of heat stroke in British soldiers at the Battle of Battenburg and calculate details about Fort McHenry such as its perimeter and number of cannons. Students explore key figures in the war and write a comic strip or timeline summarizing what they learned. They analyze the 1812 Overture and pretend to write a music review for the War Journal.

Chapter 2

This chapter discusses numerical approximation and error analysis in numerical methods. It defines error as the difference between the true value being sought and the approximate value obtained. There are two main sources of error: rounding error from representing values with a finite number of digits, and truncation error from using a finite number of terms to approximate infinite expressions. The concept of significant figures is also introduced to determine the precision of numerical methods.

Warming up1

This document discusses different subjects including Stephen Hawking, the Big Bang theory, and quotes from famous figures. It also discusses students' opinions on which subject - biology, chemistry, physics, math, or computers - is the most important and useful. While each subject has its advantages, it is difficult to say which is definitively the most important since they each contribute greatly to scientific understanding and technological progress.

Computer modeling

This presentation discusses modeling as an approach to problem solving. It outlines the key steps in the problem solving process, which includes recognizing the problem, collecting information, defining the problem, searching for solutions, evaluating solutions, and implementing a solution. Modeling can play a role at various stages of this process. The presentation also discusses mental modeling, the features of models including decisions, outcomes, structure and data, and techniques for becoming an effective modeler such as understanding problem content, model structure, realization, assessment, and implementation. Creativity is also highlighted as an important skill for successful modeling.

Computer modeling

Here are some slides on computer modeling. Very effective and well made slides. You can change the name and enjoy them in presentations amd assignments.hope any one seraching on this topic may find it helpful

Dams and flash floods

This teaching PPT was used to introduce the physics behind dams and the consequences of the Johnstown, PA flood.

Chapter 1

Mathematical modeling is a process that uses mathematical concepts and language to describe and understand real-world phenomena. This involves formulating hypotheses about the relationships and rates of change between variables, which are then expressed through differential equations. Once a mathematical model is developed, the problem becomes solving these equations, which can be analyzed through various modeling methods to predict future behavior and understand the underlying processes.

P90 x fitness guide

P90X presents a non-linear periodization model consisting of 6 phases that cycle through growth, recovery, and adaptive intensity periods to drive continuous muscle growth and strength gains. The program uses bodyweight and dynamic exercises as well as yoga and martial arts moves to build strength, power, flexibility, and cardiovascular fitness through constantly challenging movements.

War of 1812

The document discusses several activities for students to learn about the War of 1812. Students are asked to discuss in pairs and write responses about impressment and how people might react today. They also investigate the causes of heat stroke in British soldiers at the Battle of Battenburg and calculate details about Fort McHenry such as its perimeter and number of cannons. Students explore key figures in the war and write a comic strip or timeline summarizing what they learned. They analyze the 1812 Overture and pretend to write a music review for the War Journal.

Chapter 2

This chapter discusses numerical approximation and error analysis in numerical methods. It defines error as the difference between the true value being sought and the approximate value obtained. There are two main sources of error: rounding error from representing values with a finite number of digits, and truncation error from using a finite number of terms to approximate infinite expressions. The concept of significant figures is also introduced to determine the precision of numerical methods.

Warming up1

This document discusses different subjects including Stephen Hawking, the Big Bang theory, and quotes from famous figures. It also discusses students' opinions on which subject - biology, chemistry, physics, math, or computers - is the most important and useful. While each subject has its advantages, it is difficult to say which is definitively the most important since they each contribute greatly to scientific understanding and technological progress.

Computer modeling

This presentation discusses modeling as an approach to problem solving. It outlines the key steps in the problem solving process, which includes recognizing the problem, collecting information, defining the problem, searching for solutions, evaluating solutions, and implementing a solution. Modeling can play a role at various stages of this process. The presentation also discusses mental modeling, the features of models including decisions, outcomes, structure and data, and techniques for becoming an effective modeler such as understanding problem content, model structure, realization, assessment, and implementation. Creativity is also highlighted as an important skill for successful modeling.

Computer modeling

Here are some slides on computer modeling. Very effective and well made slides. You can change the name and enjoy them in presentations amd assignments.hope any one seraching on this topic may find it helpful

Modelling in physical

Mathematical modeling involves identifying key variables, parameters, and relationships to represent a physical phenomenon mathematically. The process includes analyzing the problem, formulating a model by determining input, state, and output variables and their relationships, solving the model analytically or numerically, validating and interpreting results, documenting the work, and periodically updating the model. Keeping accurate documentation at each step helps improve the model over time.

httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx

http://home.ubalt.edu/ntsbarsh/business-stat/opre/partIX.htm
Tools for Decision Analysis: Analysis of Risky Decisions
If you will begin with certainties, you shall end in doubts, but if you will content to begin with doubts, you shall end in almost certainties. -- Francis Bacon
Making decisions is certainly the most important task of a manager and it is often a very difficult one. This site offers a decision making procedure for solving complex problems step by step.It presents the decision-analysis process for both public and private decision-making, using different decision criteria, different types of information, and information of varying quality. It describes the elements in the analysis of decision alternatives and choices, as well as the goals and objectives that guide decision-making. The key issues related to a decision-maker's preferences regarding alternatives, criteria for choice, and choice modes, together with the risk assessment tools are also presented.
Professor Hossein Arsham
MENU
1. Introduction & Summary
2. Probabilistic Modeling: From Data to a Decisive Knowledge
3. Decision Analysis: Making Justifiable, Defensible Decisions
4. Elements of Decision Analysis Models
5. Decision Making Under Pure Uncertainty: Materials are presented in the context of Financial Portfolio Selections.
6. Limitations of Decision Making under Pure Uncertainty
7. Coping with Uncertainties
8. Decision Making Under Risk: Presentation is in the context of Financial Portfolio Selections under risk.
9. Making a Better Decision by Buying Reliable Information: Applications are drawn from Marketing a New Product.
10. Decision Tree and Influence Diagram
11. Why Managers Seek the Advice From Consulting Firms
12. Revising Your Expectation and its Risk
13. Determination of the Decision-Maker's Utility
14. Utility Function Representations with Applications
15. A Classification of Decision Maker's Relative Attitudes Toward Risk and Its Impact
16. The Discovery and Management of Losses
17. Risk: The Four Letters Word
18. Decision's Factors-Prioritization & Stability Analysis
19. Optimal Decision Making Process
20. JavaScript E-labs Learning Objects
21. A Critical Panoramic View of Classical Decision Analysis
22. Exercise Your Knowledge to Enhance What You Have Learned (PDF)
23. Appendex: A Collection of Keywords and Phrases
Companion Sites:
· Business Statistics
· Success Science
· Leadership Decision Making
· Linear Programming (LP) and Goal-Seeking Strategy
· Linear Optimization Software to Download
· Artificial-variable Free LP
Solution
Algorithms
· Integer Optimization and the Network Models
· Tools for LP Modeling Validation
· The Classical Simplex Method
· Zero-Sum Games with Applications
· Computer-assisted Learning Concepts and Techniques
· Linear Algebra and LP Connections
· From Linear to Nonlinear Optimization with Business Applications
· Construction of the Sensitivity Region for LP Models
· Zero Sagas in Four Dimensions
· Systems Simulation
· B.

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1
Course Learning Outcomes for Unit I
Upon completion of this unit, students should be able to:
1. Differentiate the steps of the quantitative analysis approach.
1.1 Apply quantitative analysis in a real-world situation.
1.2 Perform a break-even analysis.
Reading Assignment
Chapter 1: Introduction to Quantitative Analysis
Unit Lesson
Quantitative Analysis
As good an opening as any to a postgraduate quantitative analysis course is to confirm that yes, mathematics
is involved to improve situations and outcomes and also to stress that leadership has always been the
foundation for quantitative analysis. The answer will not make you prosper, but leading the effort in deciding
what quantitative analysis measurements mean and what to do after seeing these numbers may indeed
benefit your organization. There will be a bit more discussion on what leaders may do with quantitative
analysis after introducing some fundamentals.
To quote the authors from the textbook, “Quantitative analysis is the scientific approach to managerial
decision-making” (Render, Stair, Hanna, & Hale, 2015, p. 2). A scientific approach means calculating with
numbers. Conversely, perhaps you have worked for leaders who used guesswork or a gut feeling to decide a
numbers-based, quantitative issue. Perhaps the leader chose fortunately, and that is good; but pursuit of
good fortune becomes hazardous if you are using hope and chance to gain a good outcome. You may have
noticed this does not work well for most Las Vegas vacationers who enjoy themselves by gambling against
high odds that favor the casinos. So, quantitative analysis can have a valuable place as a decision tool if you
can make the analysis properly fit the situation.
The use of quantitative analysis to keep track, measure, analyze totals, and forecast, is older than language
itself. Sumerians, and other peoples in the Middle East, used it for grain and livestock categories. Exploration
of mathematics theory (i.e., learning how to add, subtract, multiply, and divide) made quantitative analysis
possible. Now, apply mathematics to estimate probability to see what might happen, determine averages and
percentages to see how much might be involved, and address many specific situations. As you can imagine,
just knowing a number (e.g., how much livestock a person has) may not mean much by itself. This is where
mathematics helps: The purpose of quantitative analysis is to turn raw data into meaningful information.
Formulas turn that data into something leaders can use along with considering qualitative factors such as
weather and customer demand. In theory, good leadership should do the rest.
Business Analytics
The textbook covers the terms below that you are intended to recall throughout the course:
Business analytics uses (usually large amounts of) data to make better business decisions. There are
three catego ...

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1
Course Learning Outcomes for Unit I
Upon completion of this unit, students should be able to:
1. Differentiate the steps of the quantitative analysis approach.
1.1 Apply quantitative analysis in a real-world situation.
1.2 Perform a break-even analysis.
Reading Assignment
Chapter 1: Introduction to Quantitative Analysis
Unit Lesson
Quantitative Analysis
As good an opening as any to a postgraduate quantitative analysis course is to confirm that yes, mathematics
is involved to improve situations and outcomes and also to stress that leadership has always been the
foundation for quantitative analysis. The answer will not make you prosper, but leading the effort in deciding
what quantitative analysis measurements mean and what to do after seeing these numbers may indeed
benefit your organization. There will be a bit more discussion on what leaders may do with quantitative
analysis after introducing some fundamentals.
To quote the authors from the textbook, “Quantitative analysis is the scientific approach to managerial
decision-making” (Render, Stair, Hanna, & Hale, 2015, p. 2). A scientific approach means calculating with
numbers. Conversely, perhaps you have worked for leaders who used guesswork or a gut feeling to decide a
numbers-based, quantitative issue. Perhaps the leader chose fortunately, and that is good; but pursuit of
good fortune becomes hazardous if you are using hope and chance to gain a good outcome. You may have
noticed this does not work well for most Las Vegas vacationers who enjoy themselves by gambling against
high odds that favor the casinos. So, quantitative analysis can have a valuable place as a decision tool if you
can make the analysis properly fit the situation.
The use of quantitative analysis to keep track, measure, analyze totals, and forecast, is older than language
itself. Sumerians, and other peoples in the Middle East, used it for grain and livestock categories. Exploration
of mathematics theory (i.e., learning how to add, subtract, multiply, and divide) made quantitative analysis
possible. Now, apply mathematics to estimate probability to see what might happen, determine averages and
percentages to see how much might be involved, and address many specific situations. As you can imagine,
just knowing a number (e.g., how much livestock a person has) may not mean much by itself. This is where
mathematics helps: The purpose of quantitative analysis is to turn raw data into meaningful information.
Formulas turn that data into something leaders can use along with considering qualitative factors such as
weather and customer demand. In theory, good leadership should do the rest.
Business Analytics
The textbook covers the terms below that you are intended to recall throughout the course:
Business analytics uses (usually large amounts of) data to make better business decisions. There are
three catego.

Modeling and Simulation - Model Types.pptx

There are three main types of models: physical models, mathematical models, and process models. Physical models are tangible representations of real-world objects or systems used for experimentation, analysis, or communicating ideas. Mathematical models represent systems using mathematical equations to describe, analyze, or predict behavior. Process models illustrate the sequence of activities and tasks involved in a particular process.

Chapter 8 050213 124604

This document summarizes key topics from a course on management and leadership in education. Specifically, it outlines Topic 8 which discusses decision making in schools. It describes decision making as a dynamic process that solves problems but can also create new ones. It then details the classical model of decision making as a multi-step process involving defining the problem, establishing goals, considering alternatives, and selecting the best option. Finally, it provides an overview of the assumptions behind decision making in schools and the general action cycle involved.

Instructional Strategies: Indirect Instruction in your lessons

As there are many categories of instructional strategies, this e-book focuses on indirect instruction. Indirect instruction is mainly student- centred and emphasizes on allowing students to get involved throughout a lesson by observing thus seeking their own meaning of the lesson.
In this e-book, the methods of indirect instruction that can be used in class will be discussed and explored.

Theelaboration likelihood modelof persuasion echoes theh.docx

The
elaboration likelihood model
of persuasion echoes the
heuristic-systematic model
of attitude processing by offering two paths to attitude change/formation. The
central route
of processing requires humans to use active and deliberate thought, whereas the
peripheral route
of processing occurs through nondeliberate processing not limited to, but inclusive of, heuristics. A central tenet of this model is that humans are motivated to hold correct attitudes and, as a result, there can be a number of factors that influence which processing route a person might take.
For this Discussion, view the media titled
Attitudes
and rate the messages on how persuasive you found each. In addition, consider which factors may influence central route and peripheral processing.
With these thoughts in mind:
Post by Day 4
your rating (1, least persuasive, to 5, most persuasive) of how persuasive you found each message. Then, explain which image(s) you attribute to central route processing and which image(s) you attribute to peripheral route processing and why. Finally, describe two factors that may influence when central route processing occurs and two factors that may influence when peripheral route processing occurs, and explain how. Be specific and use examples to illustrate your points.
.

2. The Logical Framework

This document discusses the logical framework approach (LFA) for project planning and monitoring and evaluation.
The LFA involves constructing a matrix called a "logframe" that summarizes key elements of a project: objectives, indicators, means of verification, and assumptions. The logframe is organized into columns representing these elements and rows representing objectives at the goal, outcome, and output levels.
The logframe helps ensure projects have clear objectives and relationships between activities and objectives. It also establishes a basis for monitoring and evaluation by defining indicators and means to measure success. Careful consideration of assumptions and risks is important as external factors could influence progress. The LFA provides a structured approach to project design, implementation, and evaluation.

A Descriptive Phase Model Of Problem-Solving Processes

This document presents a descriptive phase model of problem-solving processes. It begins by reviewing existing normative models of problem-solving, which assume idealized linear processes, and notes that real problem-solving contains errors, detours, and non-linear cycles. It then discusses the need for a descriptive model to represent empirical problem-solving processes. The paper develops such a descriptive phase model based on both a literature review of existing models and an analysis of video data of teachers solving geometry problems. The key aspects of the proposed descriptive model are that it allows for capturing the idiosyncratic sequencing of real problem-solving processes and comparing different processes through accumulation.

Esemen Matematik Penyelesaian Masalah

The document is a student's short coursework on problem solving in mathematics. It includes:
- Discussions of routine and non-routine problems, and Polya's 4-stage model for problem solving.
- Two sample math word problems that the student solves by applying Polya's model: drawing diagrams to understand and devise a plan for the first, and working backwards for the second.
- A reflection on strategies used, including checking answers through tables and reverse operations.
The student gains experience applying problem-solving approaches to self-created math questions.

Modules in mis

Here are the answers to your questions:
1. A management information system (MIS) is a computer-based system that provides information to help managers at different levels to perform their routine tasks and make strategic decisions.
2. The MIS database stores data provided by the accounting information system. In addition, both data and information are entered from the organization's external environment. The database contents come from various internal and external sources of the organization.
3. The two types of MIS software are:
- Report writing software that produces periodic and special reports
- Mathematical models that simulate various aspects of the organization's operations
4. The different types of reports in MIS are:
- Periodic reports prepared on a scheduled basis

Mis

Management information systems (MIS) provide computer-based information to organizational users. The MIS database stores data from accounting systems and the external environment. It uses report writing software to generate periodic and special reports from this data. Mathematical models also use the database to simulate operations. The reports and models help managers solve organizational problems.

RISK MANAGEMENT IN SOFTWARE ENGINEERING.ppt

This document discusses risk management in software engineering projects. It defines risk as an uncertainty that could negatively impact a project. Risk management is the process of identifying risks, analyzing them, planning mitigation actions, tracking risks, controlling deviations, and communicating about risks. The key principles of risk management are taking a global perspective, having a forward-looking view, open communication, and integrating it into the overall project management process. Risk management should be continuous throughout the project life cycle using the main steps of identify, analyze, plan, track, control, and communicate.

Risk management in software engineering

This document discusses risk management in software engineering projects. It defines risk as an uncertainty that could negatively impact a project. Risk management is the process of identifying risks, analyzing them, planning mitigation actions, tracking risks, controlling deviations, and communicating about risks. The key principles of risk management are taking a global perspective, having a forward-looking view, enabling open communication, and making risk management an integrated part of project management through continuous monitoring. Risk management methodologies involve identifying, analyzing, planning, tracking, controlling, and communicating about risks. This helps manage risks effectively in projects of all sizes.

Cognitive Psychology, Learning and Memory for IGNOU students

The triarchic theory of intelligence proposes that human intelligence involves three aspects: meta-components which control problem-solving and decision making, performance components which carry out actions, and knowledge-acquisition components which obtain new information. Robert Sternberg defined intelligence as adapting to and shaping one's environment. His theory analyzed the mind in terms of these executive, processing, and learning components to provide a more cognitive and less psychometric view of intelligence than prior approaches.

MIS 05 Decision Support Systems

The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com

200512F_CLTHE_Full

This document presents a portfolio from Dr. James Cunha Werner demonstrating his qualifications for a certificate in learning and teaching. It summarizes his experiences teaching C programming laboratories at the University of Manchester and Imperial College. The document outlines Werner's approach to designing learning activities, which involves understanding learning theories and the requirements students will face. It also describes how he carried out support for learning through constructive feedback and assessment. Diagrams and examples are provided as evidence of Werner's abilities in areas like lesson planning, student support, reflection, and using strategies like mind maps to enhance teaching.

Template abstrak & full paper seminar 50 thn

1) The document discusses using laboratory simulation in vocational high schools to model real-world problems. Simulation allows students to safely explore designs and solutions without the risks of hardware testing.
2) A key benefit of simulation is enabling students to incorporate systems from different areas and explore how design decisions in one subsystem impact overall system performance. Simulation also makes it possible to model multidomain systems.
3) While simulation has advantages like safety and enabling exploration, it also has drawbacks like experiences not always matching reality and losing hands-on activities. Overall, simulation is presented as an effective teaching method that can transfer learning to real situations.

Discussion Change ManagementChange often involves moving out.docx

Discussion : Change Management
Change often involves moving outside one’s comfort zone. The greater the change, the more discomfort it is likely to cause. The substantial changes associated with implementing major health care information technology projects can be disconcerting for individuals within health care organizations. People become comfortable with following a routine process and knowing their job expectations. For some, changes to routine may cause anxiety. Project managers must be able to apply change management methods that enable stakeholders to recognize and accept the benefits of a change. Project managers have a variety of methods at their disposal to mitigate uneasiness resulting from change, including conflict resolution, communication strategies, or team-building. With new health care technologies emerging at an increasingly rapid rate, proficiency in applying change management methods is critical.
In this Discussion, you analyze different methods that project managers employ to facilitate change.
To prepare:
Review this week’s Learning Resources on managing teams and change.
Explore the methods project managers use to expedite change in Chapter 5 of the course text
Project Management for Healthcare Information Technology
.
Consider how project managers use conflict resolution, communication, and team-building to facilitate change brought about by new projects.
Think about a personal experience in which change was handled ineffectively. What could have been done differently?
Post by tomorrow 09/06/2016, a 550 words essay in APA format with a minimum of 3 references from the list provided under required readings. Apply the level 1 headings as numbered below:
1)
A description of a personal experience in
the healthcare setting
(hospital, nursing unit, homecare, or doctor's office... etc), in which change was ineffectively managed.
2) Explain which management methods could have been applied to more successfully to facilitate the change.
Required Readings
Biafore, B. (2010).
Microsoft Project 2010: The missing manual
. Sebastopol, CA: O’Reilly.
Chapter 2, “Planning a Project” (pp. 39–57)
This chapter supplies a brief introduction on project planning. The chapter describes the contents of a project plan along with the process of creating relevant documents.
Coplan, S., & Masuda, D. (2011).
Project management for healthcare information technology
. New York, NY: McGraw-Hill.
Chapter 5, “Change Management” (pp. 193–237)
In this chapter, the authors review change management knowledge areas. The authors describe a variety of analysis methods applicable to change management processes and outputs.
Project Management Institute. (2013).
A guide to the project management body of knowledge (PMBOK guide)
(5th ed.). Newtown Square, PA: Author.
Chapter 3, “Project Management Processes” (pp. 47–61)
This chapter supplies information on managing a project that uses networked processes. The chapter .

Chapter 2

This chapter discusses numerical approximation and error analysis in numerical methods. It defines error as the difference between the true value being sought and the approximate value obtained. There are two main sources of error: rounding error from representing values with a finite number of digits, and truncation error from using a finite number of terms to approximate infinite expressions. The concept of significant figures is also introduced to determine the precision of numerical methods.

Chapter 3

The document discusses various numerical methods for finding the roots or zeros of equations, including closed and open methods. Closed methods like bisection and false position trap the root within a closed interval by repeatedly dividing the interval in half. Open methods like Newton-Raphson and secant methods use information about the nonlinear function to iteratively refine the estimated root without being restricted to an interval. The document also covers methods for equations with multiple roots like Muller's method.

Modelling in physical

Mathematical modeling involves identifying key variables, parameters, and relationships to represent a physical phenomenon mathematically. The process includes analyzing the problem, formulating a model by determining input, state, and output variables and their relationships, solving the model analytically or numerically, validating and interpreting results, documenting the work, and periodically updating the model. Keeping accurate documentation at each step helps improve the model over time.

httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx

http://home.ubalt.edu/ntsbarsh/business-stat/opre/partIX.htm
Tools for Decision Analysis: Analysis of Risky Decisions
If you will begin with certainties, you shall end in doubts, but if you will content to begin with doubts, you shall end in almost certainties. -- Francis Bacon
Making decisions is certainly the most important task of a manager and it is often a very difficult one. This site offers a decision making procedure for solving complex problems step by step.It presents the decision-analysis process for both public and private decision-making, using different decision criteria, different types of information, and information of varying quality. It describes the elements in the analysis of decision alternatives and choices, as well as the goals and objectives that guide decision-making. The key issues related to a decision-maker's preferences regarding alternatives, criteria for choice, and choice modes, together with the risk assessment tools are also presented.
Professor Hossein Arsham
MENU
1. Introduction & Summary
2. Probabilistic Modeling: From Data to a Decisive Knowledge
3. Decision Analysis: Making Justifiable, Defensible Decisions
4. Elements of Decision Analysis Models
5. Decision Making Under Pure Uncertainty: Materials are presented in the context of Financial Portfolio Selections.
6. Limitations of Decision Making under Pure Uncertainty
7. Coping with Uncertainties
8. Decision Making Under Risk: Presentation is in the context of Financial Portfolio Selections under risk.
9. Making a Better Decision by Buying Reliable Information: Applications are drawn from Marketing a New Product.
10. Decision Tree and Influence Diagram
11. Why Managers Seek the Advice From Consulting Firms
12. Revising Your Expectation and its Risk
13. Determination of the Decision-Maker's Utility
14. Utility Function Representations with Applications
15. A Classification of Decision Maker's Relative Attitudes Toward Risk and Its Impact
16. The Discovery and Management of Losses
17. Risk: The Four Letters Word
18. Decision's Factors-Prioritization & Stability Analysis
19. Optimal Decision Making Process
20. JavaScript E-labs Learning Objects
21. A Critical Panoramic View of Classical Decision Analysis
22. Exercise Your Knowledge to Enhance What You Have Learned (PDF)
23. Appendex: A Collection of Keywords and Phrases
Companion Sites:
· Business Statistics
· Success Science
· Leadership Decision Making
· Linear Programming (LP) and Goal-Seeking Strategy
· Linear Optimization Software to Download
· Artificial-variable Free LP
Solution
Algorithms
· Integer Optimization and the Network Models
· Tools for LP Modeling Validation
· The Classical Simplex Method
· Zero-Sum Games with Applications
· Computer-assisted Learning Concepts and Techniques
· Linear Algebra and LP Connections
· From Linear to Nonlinear Optimization with Business Applications
· Construction of the Sensitivity Region for LP Models
· Zero Sagas in Four Dimensions
· Systems Simulation
· B.

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1
Course Learning Outcomes for Unit I
Upon completion of this unit, students should be able to:
1. Differentiate the steps of the quantitative analysis approach.
1.1 Apply quantitative analysis in a real-world situation.
1.2 Perform a break-even analysis.
Reading Assignment
Chapter 1: Introduction to Quantitative Analysis
Unit Lesson
Quantitative Analysis
As good an opening as any to a postgraduate quantitative analysis course is to confirm that yes, mathematics
is involved to improve situations and outcomes and also to stress that leadership has always been the
foundation for quantitative analysis. The answer will not make you prosper, but leading the effort in deciding
what quantitative analysis measurements mean and what to do after seeing these numbers may indeed
benefit your organization. There will be a bit more discussion on what leaders may do with quantitative
analysis after introducing some fundamentals.
To quote the authors from the textbook, “Quantitative analysis is the scientific approach to managerial
decision-making” (Render, Stair, Hanna, & Hale, 2015, p. 2). A scientific approach means calculating with
numbers. Conversely, perhaps you have worked for leaders who used guesswork or a gut feeling to decide a
numbers-based, quantitative issue. Perhaps the leader chose fortunately, and that is good; but pursuit of
good fortune becomes hazardous if you are using hope and chance to gain a good outcome. You may have
noticed this does not work well for most Las Vegas vacationers who enjoy themselves by gambling against
high odds that favor the casinos. So, quantitative analysis can have a valuable place as a decision tool if you
can make the analysis properly fit the situation.
The use of quantitative analysis to keep track, measure, analyze totals, and forecast, is older than language
itself. Sumerians, and other peoples in the Middle East, used it for grain and livestock categories. Exploration
of mathematics theory (i.e., learning how to add, subtract, multiply, and divide) made quantitative analysis
possible. Now, apply mathematics to estimate probability to see what might happen, determine averages and
percentages to see how much might be involved, and address many specific situations. As you can imagine,
just knowing a number (e.g., how much livestock a person has) may not mean much by itself. This is where
mathematics helps: The purpose of quantitative analysis is to turn raw data into meaningful information.
Formulas turn that data into something leaders can use along with considering qualitative factors such as
weather and customer demand. In theory, good leadership should do the rest.
Business Analytics
The textbook covers the terms below that you are intended to recall throughout the course:
Business analytics uses (usually large amounts of) data to make better business decisions. There are
three catego ...

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1
Course Learning Outcomes for Unit I
Upon completion of this unit, students should be able to:
1. Differentiate the steps of the quantitative analysis approach.
1.1 Apply quantitative analysis in a real-world situation.
1.2 Perform a break-even analysis.
Reading Assignment
Chapter 1: Introduction to Quantitative Analysis
Unit Lesson
Quantitative Analysis
As good an opening as any to a postgraduate quantitative analysis course is to confirm that yes, mathematics
is involved to improve situations and outcomes and also to stress that leadership has always been the
foundation for quantitative analysis. The answer will not make you prosper, but leading the effort in deciding
what quantitative analysis measurements mean and what to do after seeing these numbers may indeed
benefit your organization. There will be a bit more discussion on what leaders may do with quantitative
analysis after introducing some fundamentals.
To quote the authors from the textbook, “Quantitative analysis is the scientific approach to managerial
decision-making” (Render, Stair, Hanna, & Hale, 2015, p. 2). A scientific approach means calculating with
numbers. Conversely, perhaps you have worked for leaders who used guesswork or a gut feeling to decide a
numbers-based, quantitative issue. Perhaps the leader chose fortunately, and that is good; but pursuit of
good fortune becomes hazardous if you are using hope and chance to gain a good outcome. You may have
noticed this does not work well for most Las Vegas vacationers who enjoy themselves by gambling against
high odds that favor the casinos. So, quantitative analysis can have a valuable place as a decision tool if you
can make the analysis properly fit the situation.
The use of quantitative analysis to keep track, measure, analyze totals, and forecast, is older than language
itself. Sumerians, and other peoples in the Middle East, used it for grain and livestock categories. Exploration
of mathematics theory (i.e., learning how to add, subtract, multiply, and divide) made quantitative analysis
possible. Now, apply mathematics to estimate probability to see what might happen, determine averages and
percentages to see how much might be involved, and address many specific situations. As you can imagine,
just knowing a number (e.g., how much livestock a person has) may not mean much by itself. This is where
mathematics helps: The purpose of quantitative analysis is to turn raw data into meaningful information.
Formulas turn that data into something leaders can use along with considering qualitative factors such as
weather and customer demand. In theory, good leadership should do the rest.
Business Analytics
The textbook covers the terms below that you are intended to recall throughout the course:
Business analytics uses (usually large amounts of) data to make better business decisions. There are
three catego.

Modeling and Simulation - Model Types.pptx

There are three main types of models: physical models, mathematical models, and process models. Physical models are tangible representations of real-world objects or systems used for experimentation, analysis, or communicating ideas. Mathematical models represent systems using mathematical equations to describe, analyze, or predict behavior. Process models illustrate the sequence of activities and tasks involved in a particular process.

Chapter 8 050213 124604

This document summarizes key topics from a course on management and leadership in education. Specifically, it outlines Topic 8 which discusses decision making in schools. It describes decision making as a dynamic process that solves problems but can also create new ones. It then details the classical model of decision making as a multi-step process involving defining the problem, establishing goals, considering alternatives, and selecting the best option. Finally, it provides an overview of the assumptions behind decision making in schools and the general action cycle involved.

Instructional Strategies: Indirect Instruction in your lessons

As there are many categories of instructional strategies, this e-book focuses on indirect instruction. Indirect instruction is mainly student- centred and emphasizes on allowing students to get involved throughout a lesson by observing thus seeking their own meaning of the lesson.
In this e-book, the methods of indirect instruction that can be used in class will be discussed and explored.

Theelaboration likelihood modelof persuasion echoes theh.docx

The
elaboration likelihood model
of persuasion echoes the
heuristic-systematic model
of attitude processing by offering two paths to attitude change/formation. The
central route
of processing requires humans to use active and deliberate thought, whereas the
peripheral route
of processing occurs through nondeliberate processing not limited to, but inclusive of, heuristics. A central tenet of this model is that humans are motivated to hold correct attitudes and, as a result, there can be a number of factors that influence which processing route a person might take.
For this Discussion, view the media titled
Attitudes
and rate the messages on how persuasive you found each. In addition, consider which factors may influence central route and peripheral processing.
With these thoughts in mind:
Post by Day 4
your rating (1, least persuasive, to 5, most persuasive) of how persuasive you found each message. Then, explain which image(s) you attribute to central route processing and which image(s) you attribute to peripheral route processing and why. Finally, describe two factors that may influence when central route processing occurs and two factors that may influence when peripheral route processing occurs, and explain how. Be specific and use examples to illustrate your points.
.

2. The Logical Framework

This document discusses the logical framework approach (LFA) for project planning and monitoring and evaluation.
The LFA involves constructing a matrix called a "logframe" that summarizes key elements of a project: objectives, indicators, means of verification, and assumptions. The logframe is organized into columns representing these elements and rows representing objectives at the goal, outcome, and output levels.
The logframe helps ensure projects have clear objectives and relationships between activities and objectives. It also establishes a basis for monitoring and evaluation by defining indicators and means to measure success. Careful consideration of assumptions and risks is important as external factors could influence progress. The LFA provides a structured approach to project design, implementation, and evaluation.

A Descriptive Phase Model Of Problem-Solving Processes

This document presents a descriptive phase model of problem-solving processes. It begins by reviewing existing normative models of problem-solving, which assume idealized linear processes, and notes that real problem-solving contains errors, detours, and non-linear cycles. It then discusses the need for a descriptive model to represent empirical problem-solving processes. The paper develops such a descriptive phase model based on both a literature review of existing models and an analysis of video data of teachers solving geometry problems. The key aspects of the proposed descriptive model are that it allows for capturing the idiosyncratic sequencing of real problem-solving processes and comparing different processes through accumulation.

Esemen Matematik Penyelesaian Masalah

The document is a student's short coursework on problem solving in mathematics. It includes:
- Discussions of routine and non-routine problems, and Polya's 4-stage model for problem solving.
- Two sample math word problems that the student solves by applying Polya's model: drawing diagrams to understand and devise a plan for the first, and working backwards for the second.
- A reflection on strategies used, including checking answers through tables and reverse operations.
The student gains experience applying problem-solving approaches to self-created math questions.

Modules in mis

Here are the answers to your questions:
1. A management information system (MIS) is a computer-based system that provides information to help managers at different levels to perform their routine tasks and make strategic decisions.
2. The MIS database stores data provided by the accounting information system. In addition, both data and information are entered from the organization's external environment. The database contents come from various internal and external sources of the organization.
3. The two types of MIS software are:
- Report writing software that produces periodic and special reports
- Mathematical models that simulate various aspects of the organization's operations
4. The different types of reports in MIS are:
- Periodic reports prepared on a scheduled basis

Mis

Management information systems (MIS) provide computer-based information to organizational users. The MIS database stores data from accounting systems and the external environment. It uses report writing software to generate periodic and special reports from this data. Mathematical models also use the database to simulate operations. The reports and models help managers solve organizational problems.

RISK MANAGEMENT IN SOFTWARE ENGINEERING.ppt

This document discusses risk management in software engineering projects. It defines risk as an uncertainty that could negatively impact a project. Risk management is the process of identifying risks, analyzing them, planning mitigation actions, tracking risks, controlling deviations, and communicating about risks. The key principles of risk management are taking a global perspective, having a forward-looking view, open communication, and integrating it into the overall project management process. Risk management should be continuous throughout the project life cycle using the main steps of identify, analyze, plan, track, control, and communicate.

Risk management in software engineering

This document discusses risk management in software engineering projects. It defines risk as an uncertainty that could negatively impact a project. Risk management is the process of identifying risks, analyzing them, planning mitigation actions, tracking risks, controlling deviations, and communicating about risks. The key principles of risk management are taking a global perspective, having a forward-looking view, enabling open communication, and making risk management an integrated part of project management through continuous monitoring. Risk management methodologies involve identifying, analyzing, planning, tracking, controlling, and communicating about risks. This helps manage risks effectively in projects of all sizes.

Cognitive Psychology, Learning and Memory for IGNOU students

The triarchic theory of intelligence proposes that human intelligence involves three aspects: meta-components which control problem-solving and decision making, performance components which carry out actions, and knowledge-acquisition components which obtain new information. Robert Sternberg defined intelligence as adapting to and shaping one's environment. His theory analyzed the mind in terms of these executive, processing, and learning components to provide a more cognitive and less psychometric view of intelligence than prior approaches.

MIS 05 Decision Support Systems

The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com

200512F_CLTHE_Full

This document presents a portfolio from Dr. James Cunha Werner demonstrating his qualifications for a certificate in learning and teaching. It summarizes his experiences teaching C programming laboratories at the University of Manchester and Imperial College. The document outlines Werner's approach to designing learning activities, which involves understanding learning theories and the requirements students will face. It also describes how he carried out support for learning through constructive feedback and assessment. Diagrams and examples are provided as evidence of Werner's abilities in areas like lesson planning, student support, reflection, and using strategies like mind maps to enhance teaching.

Template abstrak & full paper seminar 50 thn

1) The document discusses using laboratory simulation in vocational high schools to model real-world problems. Simulation allows students to safely explore designs and solutions without the risks of hardware testing.
2) A key benefit of simulation is enabling students to incorporate systems from different areas and explore how design decisions in one subsystem impact overall system performance. Simulation also makes it possible to model multidomain systems.
3) While simulation has advantages like safety and enabling exploration, it also has drawbacks like experiences not always matching reality and losing hands-on activities. Overall, simulation is presented as an effective teaching method that can transfer learning to real situations.

Discussion Change ManagementChange often involves moving out.docx

Discussion : Change Management
Change often involves moving outside one’s comfort zone. The greater the change, the more discomfort it is likely to cause. The substantial changes associated with implementing major health care information technology projects can be disconcerting for individuals within health care organizations. People become comfortable with following a routine process and knowing their job expectations. For some, changes to routine may cause anxiety. Project managers must be able to apply change management methods that enable stakeholders to recognize and accept the benefits of a change. Project managers have a variety of methods at their disposal to mitigate uneasiness resulting from change, including conflict resolution, communication strategies, or team-building. With new health care technologies emerging at an increasingly rapid rate, proficiency in applying change management methods is critical.
In this Discussion, you analyze different methods that project managers employ to facilitate change.
To prepare:
Review this week’s Learning Resources on managing teams and change.
Explore the methods project managers use to expedite change in Chapter 5 of the course text
Project Management for Healthcare Information Technology
.
Consider how project managers use conflict resolution, communication, and team-building to facilitate change brought about by new projects.
Think about a personal experience in which change was handled ineffectively. What could have been done differently?
Post by tomorrow 09/06/2016, a 550 words essay in APA format with a minimum of 3 references from the list provided under required readings. Apply the level 1 headings as numbered below:
1)
A description of a personal experience in
the healthcare setting
(hospital, nursing unit, homecare, or doctor's office... etc), in which change was ineffectively managed.
2) Explain which management methods could have been applied to more successfully to facilitate the change.
Required Readings
Biafore, B. (2010).
Microsoft Project 2010: The missing manual
. Sebastopol, CA: O’Reilly.
Chapter 2, “Planning a Project” (pp. 39–57)
This chapter supplies a brief introduction on project planning. The chapter describes the contents of a project plan along with the process of creating relevant documents.
Coplan, S., & Masuda, D. (2011).
Project management for healthcare information technology
. New York, NY: McGraw-Hill.
Chapter 5, “Change Management” (pp. 193–237)
In this chapter, the authors review change management knowledge areas. The authors describe a variety of analysis methods applicable to change management processes and outputs.
Project Management Institute. (2013).
A guide to the project management body of knowledge (PMBOK guide)
(5th ed.). Newtown Square, PA: Author.
Chapter 3, “Project Management Processes” (pp. 47–61)
This chapter supplies information on managing a project that uses networked processes. The chapter .

Modelling in physical

Modelling in physical

httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx

httphome.ubalt.eduntsbarshbusiness-statoprepartIX.htmTool.docx

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1 .docx

MSL 5080, Methods of Analysis for Business Operations 1 .docx

Modeling and Simulation - Model Types.pptx

Modeling and Simulation - Model Types.pptx

Chapter 8 050213 124604

Chapter 8 050213 124604

Instructional Strategies: Indirect Instruction in your lessons

Instructional Strategies: Indirect Instruction in your lessons

Theelaboration likelihood modelof persuasion echoes theh.docx

Theelaboration likelihood modelof persuasion echoes theh.docx

2. The Logical Framework

2. The Logical Framework

A Descriptive Phase Model Of Problem-Solving Processes

A Descriptive Phase Model Of Problem-Solving Processes

Esemen Matematik Penyelesaian Masalah

Esemen Matematik Penyelesaian Masalah

Modules in mis

Modules in mis

Mis

Mis

RISK MANAGEMENT IN SOFTWARE ENGINEERING.ppt

RISK MANAGEMENT IN SOFTWARE ENGINEERING.ppt

Risk management in software engineering

Risk management in software engineering

Cognitive Psychology, Learning and Memory for IGNOU students

Cognitive Psychology, Learning and Memory for IGNOU students

MIS 05 Decision Support Systems

MIS 05 Decision Support Systems

200512F_CLTHE_Full

200512F_CLTHE_Full

Template abstrak & full paper seminar 50 thn

Template abstrak & full paper seminar 50 thn

Discussion Change ManagementChange often involves moving out.docx

Discussion Change ManagementChange often involves moving out.docx

Chapter 3

The document discusses various numerical methods for finding the roots or zeros of equations, including closed and open methods. Closed methods like bisection and false position trap the root within a closed interval by repeatedly dividing the interval in half. Open methods like Newton-Raphson and secant methods use information about the nonlinear function to iteratively refine the estimated root without being restricted to an interval. The document also covers methods for equations with multiple roots like Muller's method.

Chapter 5

The document discusses two iterative methods for solving systems of linear equations:
1. The Jacobi method, which solves for each diagonal element using the previous iteration's values for other elements. It converges to the solution by iterating this process.
2. The Gauss-Seidel method, which sequentially updates elements using values from the current iteration, making it converge faster than the Jacobi method. Both methods decompose the matrix and iteratively solve for the unknowns until the solution converges.

Chapter 4

This chapter discusses direct methods for solving systems of linear equations, including Gauss elimination, Gauss-Jordan elimination, and LU decomposition. It provides examples of using each method to solve systems and describes the steps involved, such as putting the matrix in echelon form and using row operations. LU decomposition involves decomposing the original matrix into lower and upper triangular matrices. The chapter concludes by outlining the steps to solve a system using LU decomposition.

Capitulo 4

El documento describe los conceptos básicos de las matrices y los sistemas de ecuaciones lineales, incluyendo la notación matricial, los tipos de matrices, la multiplicación y determinante de matrices, y métodos para resolver pequeños sistemas de ecuaciones como el método gráfico, la regla de Cramer y la eliminación de incógnitas.

Expocision

Este documento presenta una introducción a las matrices y los sistemas de ecuaciones lineales. Explica la notación matricial y los tipos de matrices. Luego describe métodos para multiplicar matrices y calcular determinantes. Finalmente, resume métodos analíticos para resolver sistemas de ecuaciones lineales pequeños, como el método gráfico, la regla de Cramer y la eliminación de incógnitas.

Expocision

Este documento presenta una introducción a las matrices y los sistemas de ecuaciones lineales. Explica la notación matricial y los tipos de matrices. Luego describe métodos para multiplicar matrices y calcular determinantes. Finalmente, resume métodos analíticos para resolver sistemas de ecuaciones lineales pequeños, como el método gráfico, la regla de Cramer y la eliminación de incógnitas.

Expocision

Este documento presenta una introducción a las matrices y los sistemas de ecuaciones lineales. Explica la notación matricial y los tipos de matrices. Luego describe métodos para multiplicar matrices y calcular determinantes. Finalmente, resume métodos analíticos para resolver sistemas de ecuaciones lineales pequeños, como el método gráfico, la regla de Cramer y la eliminación de incógnitas.

Chapter 2

Chapter 2

Chapter 3

Chapter 3

Chapter 5

Chapter 5

Chapter 4

Chapter 4

Capitulo 4

Capitulo 4

Expocision

Expocision

Expocision

Expocision

Expocision

Expocision

Expocision

Expocision

- 1. Chapter 1: what is a modeling?<br />It is a process to create a model to make or understand something. A mathematical modeling is then defined as a process from the mathematical point, of view in which describes a real-world fact; the main objective is to understand that process and also to predict its behavior in the future.<br />The differential equations as mathematical modeling.<br />When the hypothesis is raised, implies the reason or rate of change of one or more variables involved. Therefore, the mathematical statement of this hypothesis is one or more equations, which involved, derivative, differential equations.<br />. Methods for solving differential equations or analyze<br />When we formulated the mathematical model, the problem is to resolve, in most cases is not easy. The modeling methods we can study summarized as follows:<br />BIBLIOGRAPY <br />http://www2.uca.es/matematicas/Docencia/FC/0206024/Apuntes/tema1_0506.pdf<br />http://mazinger.sisib.uchile.cl/repositorio/ap/ciencias_quimicas_y_farmaceuticas/apmat4a/01b.html<br />