
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
- 1Market research data analysis project
- 2Sales forecasting using time series methods
- 3Decision analysis case using decision trees
- 4Regression modeling for business prediction
- 5Dashboard 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
Recommended Textbooks
Quantitative Methods for Business
by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams
View Book DetailsBusiness Statistics: A Decision-Making Approach
by David F. Groebner, Patrick W. Shannon, and Phillip C. Fry
View Book DetailsStatistics for Business: Decision Making and Analysis
by Robert Stine and Dean Foster
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