Quantitative analysis can be challenging if you are unfamiliar with algebra, probability, or translating business problems into mathematical models, but it is manageable with practice.
What is a quantitative analysis course?
A quantitative analysis course teaches methods for using numerical information to make decisions. In a business setting, that might mean comparing uncertain outcomes, planning a project, or allocating limited resources.
The table below shows the chapters, lessons, and study hours for UPI Study’s Quantitative Analysis course.
| # | Chapter | Lessons | Hours |
|---|---|---|---|
| 1 | Strategic Management and Organizational Decision-Making | : Thinking Ahead: How the Strategic Management Process Guides Organizational Direction; : How Organizations Make Choices: Structures and Procedures Behind Organizational Decision-Making; : A Toolkit for Uncertainty: Applying Tree Analysis, Payback Methods, and Simulation to Business Choices; : Crunching Numbers for Better Outcomes: The Advantages of a Quantitative Decision-Making Approach | 2 |
| 2 | Decision Models, Risk, and Uncertainty | : Choosing the Right Framework: Types and Development of Decision-Making Models; : Decision-Making Under Certainty and Risk: Structured Approaches When Data Is Available; : Decision-Making Under Uncertainty: Strategies When Outcomes and Probabilities Are Unknown; : Knowing What Could Go Wrong: Business Risk Analysis and the Goals of Risk Management | 2 |
| 3 | Simulation in Business Analysis | : Running the Scenario Before It Happens: Simulation as a Tool for Business Problem-Solving; : Building Simulation Models: Random Numbers, Probability Distributions, and the Difference Between Static and Dynamic Approaches; : Rolling the Dice Deliberately: The Scope and Business Applications of Monte Carlo Simulation | 1.5 |
| 4 | Inventory Management and Queuing Theory | : What Goes on the Shelf: Understanding the Components of Merchandise Inventory; : The True Cost of Keeping Stock: Definitions, Methods, and Types of Inventory Costs; : Supply Meets Demand: Balancing Inventory Needs Against Cost Constraints; : Keeping Track of What You Have: Types and Objectives of Inventory Control Systems | 3.5 |
| 5 | Decision Analysis: Frameworks and Tools | : Making Decisions on Purpose: The Discipline and Structure of Business Decision Analysis; : Data That Informs Choices: Using Probability and Economic Metrics in Decision-Making; : Choosing in the Dark: Three Frameworks for Rational Decisions When No Probabilities Exist; : Probability in Action: Applying Decision Analysis When Likelihood Data Is Available | 3 |
| 6 | Decision Analysis: Bayesian Methods, Strategy, and Utility | : Revising the Estimate: How New Evidence and Updated Probabilities Sharpen Business Decisions; : Playing It Smart: Understanding and Applying Business Decision Strategies; : Reasoning Backward From Evidence: Bayes' Theorem Defined, Derived, and Applied; : Is More Data Worth It? Evaluating the Expected Value of Sample Information | 2.5 |
| 7 | Project Planning, Scheduling, and Cost Management | : Mapping the Work: Using PERT to Analyze Projects and Plan for Completion; : A Visual Timeline for Every Project: The Legacy of Henry Gantt and the Power of Bar Charts; : From Vision to Execution: The Phases, Steps, and Deliverables of Project Planning; : Assigning Responsibility: How Project Teams Are Structured and Work Is Broken Down | 2 |
| 8 | Project Risk, Critical Path, and Global Complexity | : Sources of Uncertainty in Project Timelines: Why Activity Durations Are Rarely Fixed; : Estimating Under Uncertainty: Statistical Methods for Predicting Project Completion Times; : Finding the Bottleneck: How the Critical Path Method Identifies What Cannot Be Delayed; : Every Expense Counted: Distinguishing Direct and Indirect Costs in Project Budgeting | 2.5 |
| 9 | Linear Programming: Modeling and Solving | : Constraints as Equations: Constructing Linear Programming Models From Real Business Problems; : Indivisible by Design: Using Integer Programming to Solve Budgeting and Distribution Challenges; : Solving for the Best Outcome: Putting Linear Programming to Work on Business Constraints; : Where the Optimal Lives: Why Extreme Points Are Central to Linear Programming Solutions | 2.5 |
| 10 | Sensitivity Analysis and Solution Robustness | : What Sensitivity Analysis Is and Why Every LP Solution Needs It; : How Changing Coefficients, Constraints, and Parameters Affect the Optimal Solution; : Testing the Boundaries: Sensitivity Analysis and What It Reveals About LP Optimal Solutions | 1.5 |
| 11 | LP Applications in Marketing and Finance | : Where the Budget Goes: Applying LP to Audience Targeting and Marketing Research Allocation; : LP in Financial Planning: Optimizing Budgets, Cash Flow, and Resource Allocation; : LP in Portfolio Management: Balancing Risk, Return, and Investment Strategy | 1.5 |
| 12 | Transportation Problems: Models and Methods | : Getting Goods Where They Need to Go: The Characteristics, Variants, and Solutions of Transportation Problems; : Edge Cases and Exceptions: Tackling Non-Standard Scenarios in Transportation Problem-Solving; : Starting Simple and Improving: Solving Transportation Problems from Initial Feasible Solution to Optimal Plan | 1.5 |
| 13 | Assignment Problems and Optimization | : The Assignment Problem: What It Is, Where It Appears, and Why It Matters; : Step by Step Through the Hungarian Algorithm: Solving Assignment Problems Optimally | 1 |
| ASSIGNMENT | 5 | ||
| FINAL EXAM | 2 |
How hard is quantitative analysis?
The difficulty depends partly on your starting skills. If percentages, equations, and basic probability feel comfortable, you can focus more attention on choosing and interpreting the right model. If those skills are rusty, you may need extra practice before the later topics feel natural.
The course moves across several kinds of problems rather than repeating one calculation. Decision trees and probability distributions involve uncertainty, while linear programming asks you to express an objective and its constraints mathematically. Project scheduling adds another way of thinking: tracing dependencies between activities.
The work is not just about getting a number. You also need to explain what that number means and whether the assumptions behind it fit the decision.
Which colleges have matched Quantitative Analysis? 18 colleges confirm UPI Study's Quantitative Analysis (Math 250, 3 credits), according to their equivalency pages on UPI Study. Here are 12 of them, with the course it counts as at each:
| College | Course it counts as | Credits | How it counts |
|---|---|---|---|
| Thomas Edison State University | BUS-2100 Quantitative Skills for Business | 3 | Transfers |
| Southern New Hampshire University | MAT1ELE – Mathematics Elective | 3 | In SNHU's Work & Life Experience database |
| University of the People | General Elective (UoPeople) | 3 | Transfers (Elective) |
| Penn State | MATH – Mathematics Transfer Credit Course | 3 | Transfers |
| Post University | MAT 1XX - Math Elective | 3 | Transfers |
| Strayer University | Data-Driven Decisions (MAT210) | 3 | Transfers |
| Columbia Southern University | Mathematics | 3 | Transfers |
| Gracelyn University | MATH 1332 Contemporary Mathematics | 3 | Transfers |
| UA Grantham | MA230 (Mathematical Statistics I) | 3 | Transfers |
| Crestpoint University | General Elective (Crestpoint) | 3 | Transfers (Elective) |
| Dunlap-Stone University | General Education Mathematics | 3 | Transfers |
| Indiana Tech | MA EL – Math Elective | 3 | Transfers |
Also confirmed by Maestro, Newlane, Nexford, Pathways, UMT, Woodmont.
See Quantitative Analysis on UPI Study →
ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities. Each college confirms the course when it evaluates your transcript.
Is quantitative analysis math?
Yes. Quantitative analysis uses math to describe and compare choices, although the particular methods depend on the problem. Useful preparation for Math 250 includes arithmetic, percentages, rearranging equations, reading graphs, and basic probability.
For example, suppose a choice has a 60% chance of a $100 payoff and a 40% chance of a $20 payoff. Its expected payoff is 0.60 × $100 + 0.40 × $20 = $68. That is an average across the possible outcomes, not a promise that any single outcome will be $68.
Statistics and probability are part of the toolkit, but the subject also includes optimization and decision models. For a closer look at why those methods are useful, read the advantages of quantitative analysis.
The Complete Resource for Quantitative Analysis
UPI Study has a full resource page built specifically for quantitative analysis — covering which courses count and how to get started at $250 per course with no deadlines. Your college decides how credits transfer.
See Quantitative Analysis →What does quantitative analysis include?
Math 250 includes decision analysis, simulation, inventory and waiting-line models, project planning, linear programming, sensitivity analysis, transportation problems, and assignment problems. Some topics focus on choosing under uncertainty; others focus on finding a good allocation of limited resources.
For a simple optimization example, suppose each unit of a product earns $5 and takes two hours to make. If only 10 hours are available, the constraints are 2x ≤ 10 and x ≥ 0, where x is the number of units. Maximizing 5x gives x = 5 and a payoff of $25, assuming all units can be sold and no other constraints apply.
Sensitivity analysis then asks what happens if an input changes—for instance, if available production time increases. For more on choices when outcomes are uncertain, see how to make decisions under uncertainty.
How to do quantitative analysis?
Start with the decision you need to make, then identify the information and limits that matter. As you practice, use a repeatable process:
- Define the decision and the goal, such as minimizing cost.
- List the possible choices, relevant data, and constraints.
- Choose a suitable model and work through the calculation.
- Check the result against the original problem and explain what it means.
- Consider how changes in assumptions might affect the answer.
To prepare for class, review one math skill at a time and practice with short word problems. Write down what each variable represents before calculating; this makes it easier to spot a model that does not match the scenario.
Which colleges list Quantitative Analysis on an equivalency chart?
These colleges show Quantitative Analysis (Math 250) on their equivalency charts with UPI Study. Open a college for its full chart.
| College | Equivalent course | Credits | Status |
|---|---|---|---|
| CSU | Mathematics | 3 | Confirmed on the college's chart |
| Dunlap-Stone | General Education Mathematics | 3 | Confirmed on the college's chart |
| Gracelyn | MATH 1332 Contemporary Mathematics | 3 | Confirmed on the college's chart |
| Indiana Tech | MA EL – Math Elective | 3 | Confirmed on the college's chart |
| Newlane | Quantitative Literacy GE | 3 | Confirmed on the college's chart |
| Nexford | Math Elective (Gen Ed) | 3 | Confirmed on the college's chart |
| Penn State | MATH – Mathematics Transfer Credit Course | 3 | Confirmed on the college's chart |
| Post University | MAT 1XX - Math Elective | 3 | Confirmed on the college's chart |
See the rest of the equivalency on the Quantitative Analysis course page, or open a college above for its full chart. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
Frequently Asked Questions about Quantitative Analysis
It can be challenging, particularly if algebra, probability, or word problems are new to you. Practicing those foundations and learning to identify what each model represents can make the material more approachable.
Quantitative analysis can include statistics, but it is broader than statistics alone. It also uses methods such as optimization, decision trees, and project scheduling.
Quantitative analysis in business uses numerical information and models to help evaluate decisions. For example, a business might compare expected payoffs or find a way to allocate limited production time.
Quantitative analysis in finance applies numerical methods to financial decisions. Examples include comparing potential investment outcomes and modeling how to allocate a budget under constraints.
Quantitative analysis can refer to chemistry, but it also refers to numerical decision methods in business and other fields. In chemistry, it generally means determining how much of a substance is present in a sample.
A quantitative analysis class teaches methods for using numbers and models to answer practical questions. UPI Study’s Math 250 focuses on business decisions and includes quizzes, an assignment, and a cumulative final exam.
It is the use of numerical models to compare business choices and their possible consequences. A decision tree, for example, can organize alternatives, outcomes, and probabilities so a decision-maker can compare them.
Final Thoughts on Quantitative Analysis
Before starting, try a few algebra and basic probability problems without notes. Use the topics that slow you down to make a short review plan, then practice explaining each result in plain language.
The way this actually clicks
Skip step 3 and the whole thing is wasted.
Ready to Earn College Credit?
Credit recommended by NCCRS · Self-paced · Your college decides how credits transfer · $250/course or $99/month (US)
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