Course Details

Objective

By the end of the course, participants will be able to understand

• Understand the key concepts and methods in quantitative analysis.
• Learn how to apply statistical tools to analyze and interpret data.
• Develop skills in data collection, probability theory, and sampling techniques.
• Understand and apply regression analysis and correlation to analyze relationships between variables.
• Learn how to perform hypothesis testing and interpret results.
• Gain proficiency in using quantitative techniques for decision-making and forecasting.

Content

Day 1: Introduction to Quantitative Analysis

• Overview of quantitative analysis and its applications
• Key concepts: data types, variables, and data sources
• Data collection methods: surveys, experiments, and observational studies
• Basic descriptive statistics: mean, median, mode, variance, and standard deviation
• Introduction to statistical software tools for analysis

Day 2: Probability and Probability Distributions

• Basic probability theory and rules (addition, multiplication)
• Common probability distributions: normal, binomial, Poisson
• Sampling theory and central limit theorem
• Probability in decision-making: expected value and risk analysis
• Applications of probability distributions in real-world problems

Day 3: Regression Analysis and Correlation

• Introduction to correlation and causality
• Simple linear regression: concepts, interpretation, and assumptions
• Multiple regression analysis: using multiple predictors to forecast outcomes
• Evaluating regression models: R-squared, p-values, confidence intervals
• Applications of regression in business and economics

Day 4: Hypothesis Testing and Statistical Inference

• Introduction to hypothesis testing: null and alternative hypotheses
• Types of errors (Type I and Type II) and significance levels
• Common tests: t-tests, chi-square tests, ANOVA
• Confidence intervals and margin of error
• Interpreting p-values and test results in decision-making

Day 5: Forecasting and Decision Analysis

• Introduction to forecasting methods: time series analysis, moving averages
• Exponential smoothing and ARIMA models
• Decision analysis techniques: decision trees, expected value of perfect information
• Sensitivity analysis and risk management
• Real-world applications and case studies in quantitative decision-making

NOTE:

Pre-& Post Tests will be conducted.

Case Studies, Group Exercises, Group Discussions, Last Day reviews, and assessments will be carried out.

Methodology

A highly interactive combination of lectures and discussion sessions will be managed to maximize the amount and quality of information and knowledge transfer. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. You will also be encouraged to raise your own questions and to share in the development of the right answers using your own analysis and experiences.  Tests of multiple-choice type will be made available on daily basis to examine the effectiveness of delivering the course.

Very useful Course Materials will be given.

• 30% Lectures
• 30% Workshops and work presentation
• 20% Group Work& Practical Exercises
• 20% Videos& General Discussions