Quantitative Techniques

It is applied course in statistics that is designed to provide you with the concepts and methods of statistical analysis for decision making under uncertainties

Course Introduction

Quantitative Techniques is an applied course that equips students with statistical methods and mathematical tools for decision-making in business and research environments. This course focuses on practical applications of quantitative methods to solve real-world problems, analyze data, and make informed decisions under uncertainty.

Course Content

Module 1: Introduction to Quantitative Methods

Role of quantitative techniques in decision making
Types of business and research problems
Data collection methods and sources
Measurement scales and data types
Introduction to statistical software packages

Module 2: Descriptive Statistics and Data Visualization

Measures of central tendency and dispersion
Data distribution and normality
Graphical representation of data
Dashboard creation and interpretation
Exploratory data analysis techniques

Module 3: Probability and Decision Theory

Probability concepts and applications
Decision trees and expected value
Bayesian decision making
Risk analysis and utility theory
Monte Carlo simulation methods

Module 4: Statistical Inference and Hypothesis Testing

Sampling methods and distributions
Confidence intervals and estimation
Hypothesis testing for business decisions
ANOVA and MANOVA
Non-parametric tests for business data

Module 5: Predictive Modeling and Forecasting

Correlation and regression analysis
Multiple regression models
Time series analysis and forecasting
Trend analysis and seasonal adjustments
Introduction to machine learning applications

Assignments

1
Market research data analysis project
2
Sales forecasting using time series methods
3
Decision analysis case using decision trees
4
Regression modeling for business prediction
5
Dashboard creation for business metrics

Case Studies

Case Study 1

Inventory optimization for retail business

Case Study 2

Customer segmentation using cluster analysis

Case Study 3

Demand forecasting for manufacturing

Case Study 4

Quality control implementation using statistical methods

Case Study 5

Risk assessment for financial investments

Datasets

Retail Sales Dataset

Historical sales data for retail forecasting exercises

Customer Satisfaction Survey Data

Survey responses for service quality analysis

Stock Market Historical Data

Financial time series for investment analysis

Manufacturing Quality Control Data

Production metrics and defect rates for quality analysis

Ready to Enroll in this Course?

Gain valuable skills and knowledge in Quantitative Techniques. Contact us today to learn about enrollment options, schedules, and more.

Contact for Enrollment

Data|Statistics|Research|Consultancy