Application of Statistics in Business Analytics

Explore numerical (discrete and continuous) and categorical (nominal and ordinal) data types, learn to define and convert data for analysis, and note binary data and dummy variable traps.

Explore central tendencies to summarize large datasets, learning mean, median, and mode, their advantages, challenges with outliers, and data ordering for accurate representation.

Explore how mean, median, and mode behave in symmetric and skewed distributions, and learn when to use each central tendency in real-world data such as income and housing prices.

Explore standard deviation and variability by comparing observations to the mean, derive mean deviation and variance, and apply empirical rules within normal distribution.

Explore the normal distribution, also called the glosson distribution, a bell-shaped, symmetric curve with mean, median, and mode aligned, and learn how standard deviation governs data variability and 68–95–99% rules.

Explore inferential statistics and why we need them, using sample data to represent a population. Learn how random sampling enables extrapolation, inferences, and practical analysis despite errors and outliers.

Apply interval estimates and confidence intervals in business analytics by using sample data, standard error, and the central limit theorem to bound population parameters.

Analyze the simple linear regression output, focusing on regression statistics, ANOVA, and model statistics to assess accuracy and refine predictions.

Explore multiple linear regression as a multivariate technique with two or more independent variables, using least squares, interpreting coefficients, and applying diagnostics like adjusted r-squared.