Statistical Methods for Business Decision-Making

Learning Objectives

Learn the basics of inferential statistics, including hypothesis formulation, significance testing, and p-value interpretation.

Differentiate between one-tailed vs. two-tailed tests and one-sample vs. two-sample tests to ensure the correct approach to hypothesis testing.

Recognize when to use Z-tests or T-tests based on sample size and data characteristics to validate hypotheses effectively.

Understand the use of ANOVA in comparing means across multiple groups, enabling more complex insights.

Learn to analyze relationships between categorical variables using the Chi-Square test.

Apply learned techniques in real-world scenarios to validate business insights with statistical confidence.

Overview

In this masterclass, we dive deep into statistics to address real business challenges that GlobalMart's vendor operations team faces. The primary goal is to investigate and assess the delivery performance of vendors, with a specific focus on understanding the reliability and consistency of their delivery times.

GlobalMart’s CEO has ambitious plans to promote the brand with slogans such as “Delivery, you can trust” and “7 Day Guaranteed Delivery.” However, before these bold claims can be made, the team needs data-driven insights to validate the trustworthiness and stability of vendor deliveries.

Throughout this masterclass, you would leverage statistical tools and techniques to answer critical business questions such as :

How confident can we be in promising 7-day delivery times?
Are all vendors equally reliable, or do any of them shown patterns of inconsistency?
Which vendors have demonstrated consistent delivery performance, and which ones might pose a risk to on-time delivery goals?

This masterclass is structured to provide learners with a comprehensive toolkit of statistical techniques.

Prerequisites

Basic knowledge of statistics, including mean, median, mode, and standard deviation.
Familiarity with concepts of probability and distributions (normal distribution in particular).
Some experience with data analysis and interest in business applications of statistical methods.
Basic understanding of data types and when to use categorical vs. numerical analysis techniques.