Math 250: Quantitative Analysis

Welcome to our course on Quantitative Analysis for Strategic Decision-Making. Master key quantitative tools and risk management strategies to make data-driven business decisions with confidence.

Enroll Yourself · $39.00 USD

Course curriculum

1. Introduction
1. The Planning Process
2. Environmental Scanning
3. Assemble a Strategy
4. Implement the Strategy
5. Monitor and Evaluate the Strategy
6. Lesson Summary
7. Learning Outcome
8. Quiz 1.1
1. Decision Making
2. Defining the Problem
3. Identifying Limiting Factors
4. Develop Potential Alternatives
5. Analyze the Alternatives
6. Implement the Alternative
7. Establish a Control and Evaluation System
8. Lesson Summary
9. Learning Outcome
10. Quiz 1.2
1. Decision Making Tools
2. The Decision Tree
3. Payback Analysis
4. Simulation
5. Lesson Summary
6. Learning Outcomes
7. Quiz 1.3
1. Quantitative Approach Benefits
2. Quantitative Decision-Making
3. 1. Decision Trees
4. 2. Network Analysis
5. 3. Simulation Models
6. 4. Optimization Models
7. Lesson Summary
8. Quiz 1.4
1. When Do We Have to Make Decisions?
2. How Do We Make Decisions?
3. Examples of Rational Models
4. Intuitive Decisions
5. Developing Your Model
6. Lesson Summary
7. Quiz 1.5
1. What Is Certain
2. Weighing Risk
3. Several Perspectives
4. Lesson Summary
5. Learning Outcome
6. Quiz 1.6
1. What Is Risk?
2. The Five Risk Categories Analyzed
3. Managing Risk
4. Mitigating Risk
5. Lesson Summary
6. Quiz 1.7
1. Introduction
1. Simulation to Solve Problems
2. Types of Tools
3. Advantages Versus Disadvantages
4. Lesson Summary
5. Quiz 2.1
1. Making Guesses About the Future
2. The Usefulness of Being Random
3. Probability Distributions
4. The Value of Computer Simulations
5. Lesson Summary
6. Quiz 2.2
1. Introduction
2. Static Simulation
3. Dynamic Simulation
4. lesson Summary
5. Summary
6. Quiz 2.3
1. What Is a Monte Carlo Simulation?
2. A Brief History of the Monte Carlo Simulation
3. How the Monte Carlo Simulation Works
4. Advantages of the Monte Carlo Simulation
5. Common Applications of the Monte Carlo Simulation
6. Lesson Summary
7. Quiz 2.4
1. Introduction
1. Merchandise Inventory
2. Goods in Transit
3. Goods in Consignment
4. Goods Damaged or Obsolete
5. Lesson Summary
6. Learning Outcomes
7. Quiz 3.1
1. Inventory Costs
2. Ordering Costs
3. Carrying Costs
4. Shortage Costs
5. COGS
6. Lesson Summary
7. Learning Outcomes
8. Quiz 3.2
1. Definition
2. Demand
3. Cost
4. Technology
5. Lesson Summary
6. Quiz 3.3
1. Overview of Inventory and Control Systems
2. Continuous Inventory Systems
3. Periodic Inventory Systems
4. ABC Classification System
5. Lesson Summary
6. Quiz 3.4
1. Inventory Purchases Defined
2. Before Recording
3. Recording Inventory Purchases
4. Lesson Summary
5. Quiz 3.5
1. Decisions About the Supply Chain
2. Bullwhip Effect
3. Inventory Positioning
4. Packaging and Materials
5. Lesson Summary
6. Quiz 3.6
1. Introduction
1. Patience for Purchases
2. Losing Customers
3. Less Efficient
4. Bad Reputation
5. Resolutions
6. Lesson Summary
7. Quiz 4.1
1. What Is a Waiting-Line System?
2. Optimal Waiting Line
3. Types of Waiting Lines
4. Waiting-Line Phases
5. Lesson Summary
6. Quiz 4.2
1. The Business Economics of Waiting Lines
2. Optimal Waiting Line
3. Performance Measures
4. Bring on the Distractions
5. Lesson Summary
6. Quiz 4.3
1. Introduction
1. Decision Analysis
2. Value and Payoffs
3. Uncertainty and States of Nature
4. Lesson Summary
5. Quiz 5.1
1. Business Decisions
2. Expected Value
3. Business Cycle
4. Lesson Summary
5. Quiz 5.2
1. Decision Making
2. Optimistic Approach
3. Conservative Approach
4. Minimax Approach
5. Lesson Summary
6. Quiz 5.3
1. Probability Data Definition
2. Calculating Probability
3. Approaches to Probability
4. Lesson Summary
5. Quiz 5.4
1. Business Decisions
2. Decision Trees
3. Payoff Tables
4. Lesson Summary
5. Quiz 5.5
1. Business Decisions
2. Expected Value
3. Perfect Information
4. Lesson Summary
5. Quiz 5.6
1. Making Decisions
2. Information
3. Revised Probability
4. Lesson Summary
5. Quiz 5.7
1. What Is a Decision Strategy?
2. Automatic Decisions
3. Decision Trees
4. Lesson Summary
5. Quiz 5.8
1. More Accurate Predictions
2. Bayes' Theorem
3. Using the Math
4. Example
5. Lesson Summary
6. Quiz 5.9
1. Information Cost
2. Perfect & Sample Information
3. Calculations
4. Lesson Summary
5. Quiz 5.10
1. Driven by Utility
2. What Is Utility?
3. Upgrading Efficiency
4. Real-Life Examples of Pursuing Utility
5. Lesson Summary
6. Quiz 5.11
1. Introduction
1. Never Enough Time
2. PERT
3. Types of Time
4. Lesson Summary
5. Learning Outcomes
6. Quiz 6.1
1. Blamestorming
2. Henry Gantt
3. The Gantt Chart
4. Lesson Summary
5. Lesson Objectives
6. Quiz 6.2
1. Why Manage a Project?
2. Planning a Project
3. Performing a Project
4. Learning from a Project
5. Lesson Summary
6. Quiz 6.3
1. The Project Team
2. Roles of the Project Team
3. Member Responsibilities
4. Work Breakdown Structure
5. The Responsibility Assignment Matrix (RACI)
6. Lesson Summary
7. Learning Outcomes
8. Quiz 6.4
1. The Three Types of Estimates
2. Purpose of Three Estimates
3. Lesson Summary
4. Quiz 6.5
1. Recognizing Uncertainty
2. Strategies
3. Identifying and Breaking Down Work
4. Applying Probabilistic Scheduling
5. Lesson Summary
6. Quiz 6.6
1. What Is Critical Path Methodology?
2. How Is Critical Path Determined?
3. Managing Critical Path
4. Crashing Versus Fast Tracking
5. Lesson Summary
6. Quiz 6.7
1. Costs and Project Management
2. Direct Costs
3. Indirect Costs
4. Lesson Summary
5. Quiz 6.8
1. Sensitive Issues in Culture
2. Global Issues in Project Management
3. Breaking Down the Issues
4. Balancing the Issues
5. Lesson Summary
6. Quiz 6.9
1. Introduction
1. Linear Programming
2. Step 1: Define Your Variables
3. Step 2 - Define the Objective Function
4. Step 3 - Writing the Constraints
5. Step 5 - Finding the Solution
6. Lesson Summary
7. Quiz 7.1
1. What Is Integer Linear Programming?
2. Description of the Components of the Equation
3. Formula Used for Integer Linear Programming
4. application of the Formula of Integer Linear Programming
5. Break Even
6. Design of the Equation
7. Lesson Summary
8. Quiz 7.2
1. Introduction
1. Why Use Linear Programming?
2. Linear Programming Steps
3. Solution to the Example LP
4. Lesson Summary
5. Quiz 8.1
1. Definition
2. Advantages of Extreme Points in Problem Solving
3. Summary
4. Quiz 8.2
1. Linear Programming Models
2. Example: Maximizing Profit
3. Mathematical Problem Representation
4. Problem Solving with R: Data Input
5. Problem Solving with R: Data Output
6. Lesson Summary
7. Quiz 8.3
1. Introduction
1. What Is Sensitivity Analysis?
2. Sensitivity Analysis in Action
3. The Demand Equation
4. Changing the Values in the Demand Equation
5. Lesson Summary
6. Quiz 9.1
1. Safety-Wise Optimal Solutions
2. Graphical Sensitivity Analysis
3. Objective Function Coefficient Change
4. Right-Hand Side Value Change
5. Lesson Summary
6. Quiz 9.2
1. Introduction
1. Linear Programming for Marketing
2. What is Linear Programming?
3. Requirements for Linear Programming
4. How Can Linear Programming Be Used for Media Selection or Market Research?
5. Limitations of Linear Programming
6. Lesson Summary
7. Quiz 10.1
1. How Corporate Finance Decisions are Made
2. Using Linear Programming in Corporate Finance Decisions
3. Portfolio Selection
4. Financial Mix Strategy
5. Financial Planning
6. Lesson Summary
7. Quiz 10.2
1. Introduction
1. The Transportation Problem
2. Types
3. Solutions
4. Lesson Summary
5. Quiz 11.1
1. Transportation
2. Unequal Supply and Demand
3. Northwest Corner Rule
4. Minimum Cell Cost
5. Unacceptable Routes
6. Maximizing Objectives
7. Lesson Summary
8. Quiz 11.2
1. Cost As A Priority
2. Constraints
3. Cost Information
4. Solve the Problem
5. Lesson Summary
6. Quiz 11.3
1. What's the Problem?
2. Transportation Simplex Algorithm
3. What It Looks Like
4. Least Cost Method
5. Optimality Test
6. Lesson Summary
7. Quiz 11.4
1. The Hungarian Algorithm
2. Hungarian Algorithm Steps
3. Hungarian Algorithm Application
4. Lesson Summary
5. Quiz 11.5
1. Assignment
1. Final Exam

About this course

$39.00
345 lessons
0 hours of video content

Ch 1 - Quantitative Decision Making and Risk Analysis

Ch 1.1 - The Strategic Management Process

Ch 1.2 - The Decision Making Process for Organizations

Ch 1.3 - Quantitative Decision Making Tools: Decision Trees, Payback Analysis & Simulations

Ch 1.4 - The Quantitative Approach to Decision Making: Methods, Purpose & Benefits

Ch 1.5 - Decision Making Models: Definition, Development & Types

Ch 1.6 - Decision Making for Managers: Certainty, Risk & Uncertainty

Ch 1.7 - Risk Analysis & Risk Management in Business: Overview, Objectives & Comparison

Ch 2 - Simulation in Quantitative Analysis

Ch 2.1 - Using Simulation to Analyze and Solve Business Problems

Ch 2.2 - The Role of Probability Distributions, Random Numbers & the Computer in Simulations

Ch 2.3 - Static Vs Dynamic Simulation in Quantitative Analysis

Ch 2.4 - The Monte Carlo Simulation: Scope & Common Applications

Ch 3 - Inventory Models

Ch 3.1 - Items that Make Up Merchandise Inventory

Ch 3.2 - Inventory Cost: Definition, Methods & Types

Ch 3.3 - What Is Inventory Management? - Demand vs. Cost

Ch 3.4 - Inventory Control Systems: Types & Purpose

Ch 3.5 - Accounting for Inventory Purchases

Ch 3.6 - How Inventory Decisions Affect Other Areas of the Supply Chain

Ch 4 - Waiting Line Models & Queueing Theory

Ch 4. 1 - Waiting-Line Problems: Where They Occur & Their Effect on Business

Ch 4.2 - The Structure of a Waiting-Line System & Queuing Theory in Business

Ch 4.3 - The Business Economics of Waiting Lines

Ch 5 - Decision Analysis in Business

Ch 5.1 - The Decision Analysis Approach to Decision Making in Business

Ch 5.2 - Making Business Decisions Using Probability Information & Economic Measures

Ch 5.3 - Decision Making Without Probabilities: Optimistic, Conservative & Minimax Approaches

Ch 5.4 - Business Decision Analysis for Cases with Probability Data

Ch 5.5 - Analyzing Business Problems Using Decision Trees & Payoff Tables

Ch 5.6 - Expected Values of Perfect Information in Business

Ch 5.7 - Using New Information & Revised Probability Values to Make Business Decisions

Ch 5.8 - What Is a Decision Strategy in Business?

Ch 5.9 - Bayes' Theorem: Definition, Formula & Usage

Ch 5.10 - Evaluating the Expected Value of Sample Information

Ch 5.11 - Using Utility in Business Decision Making

Ch 6 - Project Scheduling & Management

Ch 6.1- PERT Analysis & Project Completion

Ch 6.2 - Gantt Charts & Bar Graphs: Henry Gantt's Contributions to Management

Ch 6.3 - Project Planning: Steps, Process & Return

Ch 6.4 - The Project Team and Work Breakdown Structure

Ch 6.5 - Converting Optimistic, Most Probable & Pessimistic Time Estimates

Ch 6.6 - Estimating Projects with Uncertain Activity Times

Ch 6.7 Critical Path Method: Definition, Analysis & Examples

Ch 6.8 - Direct and Indirect Costs in Project Management

Ch 6.9 - Global and Diversity Issues in Project Management

Ch 7 - Integer Programming

Ch 7.1 - Developing Linear Programming Models for Simple Problems

Ch 7.2 - Applications of Integer Linear Programming: Fixed Charge, Capital Budgeting & Distribution System Design Problems

Ch 8 - Linear Programming

Ch 8.1 - Using Linear Programming to Solve Problems

Ch 8.2 - The Importance of Extreme Points in Problem Solving

Ch 8.3 - Interpreting Computer Solutions of Linear Programming Models

Ch 9 - Sensitivity Analysis in Business

Ch 9.1 - Sensitivity Analysis: Definition, Uses & Importance

Ch 9.2 - Graphical Sensitivity Analysis for Variable Linear Programming Problems

Ch 10 - Linear Programming Applications

Ch 10.1 - Marketing Applications of Linear Programs for Media Selection & Marketing Research

Ch 10.2 - Financial Applications of Linear Programs for Portfolio Selection, Financial Planning & Financial Mix Strategy

Ch 11 - Distribution & Network Models

Ch 11.1 - The Transportation Problem: Features, Types, & Solutions

Ch 11.2 - Handling Transportation Problems & Special Cases

Ch 11.3 - Using the Minimum-Cost Method to Solve Transportation Problems

Ch 11.4 - Using the Transportation Simplex Method to Solve Transportation Problems

Ch 11.5 - Using the Hungarian Algorithm to Solve Assignment Problems

Assignment

Final Exam

About this course