Topic:Master of Business Administration

Introduction

This document will link and organize the information found here on WikiMedia and elsewhere on the internet to provide a comprehensive business education such as one provided by the top business schools in the world.
A Master in Business Administration (MBA) is unlike many other academic programs. While most Master programs provide a further specialization within a particular school or discipline (such as biology), an MBA is typically interdisciplinary, drawing from the fields of psychology, sociology, economics, accounting and finance.
Most of the materials found within an MBA program are not unique to business. Rather, an MBA provides exposure to the diverse ideas which are most useful to a person who is interested in succeeding in business within an easy-to-learn two-year bundle. Completing an MBA requires a substantial time commitment; if you work through all the concepts referenced, then one can expect to dedicate around 2,000+ hours. Likewise, familiarity with spreadsheets such as Excel is necessary.

Core Learning Projects Explanation

Many MBA programs also have coursework on advanced topics based on these core subjects. Other diverse subjects (such as psychology) also may be included in an MBA curriculum.
This listing of core learning projects attempts to replicate that core curriculum.

Introduction to probability and statistics

Getting started

The grounding for many further concepts in business, as in other applied disciplines, is a thorough understanding of statistics. A good free source for understanding probability is Introduction to Probability. In particular regression analysis is going to be useful. Regression is fundamental to the concept of Beta in Finance and conjoint analysis in Marketing. In addition, the statistical concepts of standard deviation and variance are the fundamental concepts which underly the principle of pooling risk in operations management.
The directions on how to perform a regression analysis in Excel can be found online at Regression Analysis Using Microsoft Excel