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ECO729Social Sciences2 Unitsintermediate

Applied Quantitative Analysis

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.

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156h
Study Time
13
Weeks
12h
Per Week
intermediate
Math Level
Course Keywords
Quantitative AnalysisStatistical TheoryLinear ProgrammingForecastingDecision Analysis

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level
Builds on foundational knowledge
65%
intermediate
📊
Math Level
Moderate Math
🔬
Learning Type
Hands-on Practice

Course Topics

Key areas covered in this course

1

Statistical Theory

2

Descriptive Statistics

3

Probability Applications

4

Linear Programming

5

Forecasting Methods

6

Inventory Control

7

Quantitative Research

8

Data Analysis Techniques

9

Statistical Software

Total Topics9 topics

Requirements

Knowledge and skills recommended for success

Basic Statistics

Introductory Economics

💡 Don't have all requirements? Don't worry! Many students successfully complete this course with basic preparation and dedication.

Assessment Methods

How your progress will be evaluated (3 methods)

assignments

Comprehensive evaluation of course material understanding

Written Assessment

tutor-marked assessments

Comprehensive evaluation of course material understanding

Written Assessment

final examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

Data Analyst

Apply your skills in this growing field

Business Analyst

Apply your skills in this growing field

Operations Manager

Apply your skills in this growing field

Financial Analyst

Apply your skills in this growing field

Management Consultant

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

FinanceManufacturingLogisticsHealthcareRetail

Study Schedule Beta

A structured 13-week journey through the course content

Week
1

Module 1: Statistical Theory, Descriptive Statistics and Probability Applications

3h

Unit 1: Statistical Theory and Inference

3 study 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
2

Module 1: Statistical Theory, Descriptive Statistics and Probability Applications

3h

Unit 2: Overview of Descriptive Statistics

3 study hours
  • •Understand univariate methods for categorical variables.
  • •Learn bivariate methods for categorical variables.
  • •Define descriptive statistics and its importance.
Week
3

Module 1: Statistical Theory, Descriptive Statistics and Probability Applications

3h

Unit 3: Probability Applications

3 study hours
  • •Define probability concepts and terminology.
  • •Distinguish between different probability formulas.
  • •Apply probability rules to solve practical problems.
Week
4

Module 2: Quantitative Techniques and Linear Programming

3h

Unit 1: Overview of Quantitative Techniques

3 study hours
  • •Describe the meaning of quantitative techniques.
  • •Understand various quantitative technique approaches.
  • •Learn how to develop a quantitative analysis framework.
Week
5

Module 2: Quantitative Techniques and Linear Programming

3h

Unit 2: Linear Programming Graphical Method

3 study 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
6

Module 2: Quantitative Techniques and Linear Programming

3h

Unit 3: Simplex Method

3 study 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
7

Module 2: Quantitative Techniques and Linear Programming

3h

Unit 4: Transportation Model

3 study 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
8

Module 3: Forecasting, Decision and Inventory Analysis

3h

Unit 1: Forecasting and Decision Analysis

3 study hours
  • •Explain the types of forecasts models.
  • •Understand the steps in Decision Making.
  • •Know the types of decision-making environments.
Week
9

Module 3: Forecasting, Decision and Inventory Analysis

3h

Unit 2: Demonstrate Forecasting Methods

3 study hours
  • •Demonstrate the various forecasting methods.
  • •Forecast using data sets and apply any of the forecast methods.
  • •Discuss seasonality issues in forecasting.
Week
10

Module 3: Forecasting, Decision and Inventory Analysis

3h

Unit 3: Deterministic Inventory Control Models

3 study 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
11

Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis

3h

Unit 1 An Overview of Quantitative Research

3 study hours
  • •Define quantitative research.
  • •Distinguish between quantitative and qualitative research.
  • •Determine the appropriate measurement scale for a research problem.
Week
12

Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis

3h

Unit 2 Quantitative Data Concepts

3 study hours
  • •Define quantitative data and its characteristics.
  • •Describe common methods of quantitative data collection.
  • •Distinguish between primary and secondary data in research methods.
Week
13

Module 4: Data Analysis Techniques and Statistical Software in Applied Quantitative Analysis

3h

Unit 3 Data Analysis Tools in Applied Quantitative Techniques

3 study 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.

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review statistical theory and inference concepts from Module 1, focusing on F-tests and regression analysis.

2

Practice solving linear programming problems using both graphical and simplex methods (Module 2).

3

Master forecasting techniques (moving averages, exponential smoothing) and apply them to sample datasets (Module 3).

4

Understand the assumptions and limitations of each quantitative technique.

5

Familiarize yourself with statistical software (Excel, SPSS) and practice data analysis.

6

Review key concepts in quantitative research, including validity, reliability, and generalizability (Module 4).

7

Solve all tutor-marked assignments (TMAs) and review feedback.

8

Create concept maps linking units within each module to reinforce understanding.

9

Practice applying quantitative techniques to real-world economic and business scenarios.

10

Allocate sufficient time for revision and practice questions in the weeks leading up to the exam.

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