Applied Quantitative Analysis
- Social Sciences
- 700 level
- 2 credit units
- 202 pages
- 13 units
This course on Applied Quantitative Analysis equips students with practical quantitative techniques essential for economic analysis and business decision-making. It covers statistical theory, descriptive statistics, probability applications, and various quantitative techniques. Students will learn linear programming methods, forecasting, decision analysis, and inventory control models. The course also explores data analysis techniques and the use of statistical software, enabling students to apply these tools to real-world economic and business problems.
About this course
- Difficulty
- Intermediate
- Study hours
- 156 hours
- Maths
- Intermediate
- Content
- Theoretical, practical, problem solving
- Practical work
- Yes
- Basic Statistics
- Introductory Economics
- Assignments
- Tutor marked assessments
- Final examination
What you'll read
The real module and unit structure of ECO729, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
ECO729 · UNIT 1: STATISTICAL THEORY AND INFERENCE
Statistical models, once specified, can be tested to see whether they provide useful inferences for new data sets. Testing a hypothesis using the data that was used to specify the model is a fallacy, according to the natural science of Bacon and the scientific method of Peirce.
What you should be able to do
- Apply statistical theory and inference to economic problems.
- Utilize descriptive statistics to summarize and interpret data.
- Formulate and solve linear programming problems.
- Apply forecasting methods for economic and business planning.
- Analyze decision-making environments and apply appropriate techniques.
- Apply inventory control models to optimize resource management.
- Use statistical software for data analysis and interpretation.
What it prepares you for
- Data Analyst
- Business Analyst
- Operations Manager
- Financial Analyst
- Management Consultant
- Finance
- Manufacturing
- Logistics
- Healthcare
- Retail
- Microsoft Excel
- SPSS
- STATA
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 2: Quantitative Techniques and Linear Programming
Unit 3: Simplex Method
Simplex method requires understanding of linear algebra and iterative calculations, which can be challenging for students without a strong mathematical background.
- Module 3: Forecasting, Decision and Inventory Analysis
Unit 3: Deterministic Inventory Control Models
Deterministic inventory control models require applying first order difference equations which can be complex and abstract.
A suggested way through it
13 weeks, about 39 hours in total. Yours will differ.
- Week 1Module 1: Statistical Theory, Descriptive Statistics and Probability Applications
Unit 1: Statistical Theory and Inference · 3 hours
Read the introduction to understand the importance of statistical theory.. Define statistical inference and its applications.. Explore basic statistical tools like F-test and linear regression..
- Week 2Module 1: Statistical Theory, Descriptive Statistics and Probability Applications
Unit 2: Overview of Descriptive Statistics · 3 hours
Understand univariate methods for categorical variables.. Learn bivariate methods for categorical variables.. Define descriptive statistics and its importance..
- Week 3Module 1: Statistical Theory, Descriptive Statistics and Probability Applications
Unit 3: Probability Applications · 3 hours
Define probability concepts and terminology.. Distinguish between different probability formulas.. Apply probability rules to solve practical problems..
- Week 4Module 2: Quantitative Techniques and Linear Programming
Unit 1: Overview of Quantitative Techniques · 3 hours
Describe the meaning of quantitative techniques.. Understand various quantitative technique approaches.. Learn how to develop a quantitative analysis framework..
- Week 5Module 2: Quantitative Techniques and Linear Programming
Unit 2: Linear Programming Graphical Method · 3 hours
Understand the requirements of a Linear Programming Problem.. Learn how to formulate a typical Linear Programming Problem.. Solve Linear Programming Problems using the graphical method..
- Week 6Module 2: Quantitative Techniques and Linear Programming
Unit 3: Simplex Method · 3 hours
Understand the conditions for applying the simplex method.. Learn the steps to solve linear programs using the simplex method.. Practice solving linear programming problems using the simplex method..
- Week 7Module 2: Quantitative Techniques and Linear Programming
Unit 4: Transportation Model · 3 hours
Understand the structure of a typical transportation model.. Learn how to set up a transportation model.. Solve transportation models using the Northwest Corner Rule..
- Week 8Module 3: Forecasting, Decision and Inventory Analysis
Unit 1: Forecasting and Decision Analysis · 3 hours
Explain the types of forecasts models.. Understand the steps in Decision Making.. Know the types of decision-making environments..
- Week 9Module 3: Forecasting, Decision and Inventory Analysis
Unit 2: Demonstrate Forecasting Methods · 3 hours
Demonstrate the various forecasting methods.. Forecast using data sets and apply any of the forecast methods.. Discuss seasonality issues in forecasting..
- Week 10Module 3: Forecasting, Decision and Inventory Analysis
Unit 3: Deterministic Inventory Control Models · 3 hours
Define inventory and explain what inventory control is all about.. Apply first order difference equations to estimate Inventory Control and EOQ model.. Apply modern inventory control models..
- Week 11Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis
Unit 1 An Overview of Quantitative Research · 3 hours
Define quantitative research.. Distinguish between quantitative and qualitative research.. Determine the appropriate measurement scale for a research problem..
- Week 12Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis
Unit 2 Quantitative Data Concepts · 3 hours
Define quantitative data and its characteristics.. Describe common methods of quantitative data collection.. Distinguish between primary and secondary data in research methods..
- Week 13Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis
Unit 3 Data Analysis Tools in Applied Quantitative Techniques · 3 hours
Define the concept Statistical Software.. Describe the benefits and uses of software programs in statistical analysis of quantitative data.. Understand the use of Microsoft excel in statistical data analysis..
Preparing for the exam
- Review statistical theory and inference concepts from Module 1, focusing on F-tests and regression analysis.
- Practice solving linear programming problems using both graphical and simplex methods (Module 2).
- Master forecasting techniques (moving averages, exponential smoothing) and apply them to sample datasets (Module 3).
- Understand the assumptions and limitations of each quantitative technique.
- Familiarize yourself with statistical software (Excel, SPSS) and practice data analysis.
- Review key concepts in quantitative research, including validity, reliability, and generalizability (Module 4).
- Solve all tutor-marked assignments (TMAs) and review feedback.
- Create concept maps linking units within each module to reinforce understanding.
- Practice applying quantitative techniques to real-world economic and business scenarios.
- Allocate sufficient time for revision and practice questions in the weeks leading up to the exam.
Questions students ask about this course
What is ECO729 about?
This course on Applied Quantitative Analysis equips students with practical quantitative techniques essential for economic analysis and business decision-making. It covers statistical theory, descriptive statistics, probability applications, and various quantitative techniques. Students will learn linear programming methods, forecasting, decision analysis, and inventory control models. The course also explores data analysis techniques and the use of statistical software, enabling students to apply these tools to real-world economic and business problems.
How many units does ECO729 have?
ECO729, Applied Quantitative Analysis, has 13 units across 4 modules, over 202 pages of course material. You can read it one unit at a time.
How many credit units is ECO729?
ECO729 carries 2 credit units, at 700 level in Social Sciences.
Is ECO729 hard?
ECO729 is rated intermediate level, with intermediate mathematical content. It is mostly theoretical, practical and problem solving work, and it has a practical component.
How long does ECO729 take to study?
About 156 hours of study, spread across its 13 units.
How is ECO729 assessed?
ECO729 is assessed by assignments, tutor marked assessments and final examination.
What do I need before starting ECO729?
Basic Statistics Introductory Economics
What can I do with ECO729?
Data Analyst, Business Analyst, Operations Manager, Financial Analyst and Management Consultant.