Data|Statistics|Research|Consultancy

Quantitative Techniques

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

Recommended Textbooks

Quantitative Methods for Business

by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams

View Book Details

Business Statistics: A Decision-Making Approach

by David F. Groebner, Patrick W. Shannon, and Phillip C. Fry

View Book Details

Statistics for Business: Decision Making and Analysis

by Robert Stine and Dean Foster

View Book Details

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