This module covers the fundamentals of descriptive statistics for business applications. Students will learn about different types of data, measurement scales, and techniques for organizing qualitative and quantitative data. The module explores graphic representation methods, measures of central tendency, position measurements, and measures of dispersion. Students will also gain practical experience applying descriptive statistics using laptop computers, enabling them to effectively summarize and present business data for decision-making purposes.

This module examines discrete probability distributions and their applications in business statistics. Students will develop an understanding of discrete random variables, density and cumulative probability functions, and how to calculate the mean and variance of discrete random variables. The module covers fundamental properties of probability distributions with special focus on the Poisson distribution and Binomial distribution, which are particularly useful for analyzing count data and success/failure scenarios in business contexts.

This module focuses on continuous probability distributions and their importance in business analytics. Students will learn about continuous random variables, density and cumulative probability functions, and methods for calculating the mean and variance of continuous random variables. The module explores fundamental properties of continuous distributions with particular emphasis on the Uniform distribution and Normal distribution. Students will develop skills in applying these distributions to model and analyze various business phenomena involving continuous measurements.

This module covers statistical estimation techniques essential for business decision-making under uncertainty. Students will learn how to calculate confidence intervals for population means and proportions, determining the reliability of sample-based estimates. The module also addresses how to calculate the sample size required for a given estimation error, ensuring research results meet precision requirements. Additionally, students will study Student's t distribution and its application when working with small samples or when population standard deviation is unknown, a common scenario in business research.