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GMAC FUNDAMENTALS

Kaplan & GMAC Pre-MBA Statistics Fundamentals Course

$99
Almost ready to start business school? With Statistics Fundamentals, you'll level up your statistics knowledge before entering the classroom so that you can hit the ground running in your MBA or business master's program. See More Details
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What's Included

  • Hours of self paced pre-MBA statistics lessons
  • Access on mobile, desktop, and tablet
  • Expert lectures from real business school professors
  • Quizzes and assessments throughout
  • Lessons based on real-world business scenarios
  • Ability to drill down and focus on the content you need
  • 24/7 on demand access

Product Details

Start Strong In Business School With GMAC Statistics Fundamentals Almost ready to start business school? With Statistics Fundamentals, you'll level up your statistics knowledge before entering the classroom so that you can hit the ground running in your MBA or business master's program.

Classes That Fit In Your Pocket Built on the Canvas learning platform, each pre-MBA statistics lesson is fully mobile-responsive and can be taken anytime, anywhere. Our business professors are in your pocket and available to teach you key statistics topics—on your schedule.

We'll Keep You On Track Assignments help you practically apply your new skills and measure your progress by starting and ending each topic with a pre-test and post-test. Knowledge checks throughout the course test your understanding of the content as you go, and a final exam reviews everything you've learned.

Master the Basics of Business School Statistics

We'll cover all of the topics you need for business school, including:

  • Descriptive statistics
  • Data visualization
  • Bivariate numerical data
  • Randomization
  • Sampling
  • Experiments
  • Probability theory
  • Normal distributions

Statistics Fundamentals: Course Overview

Starting from the basics of descriptive Statistics and building to more complex topics like probability theory or normal modeling, this course provides an introduction to Statistics concepts and the practices used in statistical analysis. The course is comprised of self-paced lessons that were designed and delivered by experts. The combination of high-quality assessments, written material, rich media, and application-based practice builds students' skills in the language and math of Statistics as well as the usage of Excel and similar spreadsheet software.

Course Learning Outcomes

  • Develop fluency with the basic language of Statistics
  • Present and describe quantitative and categorical data distributions using appropriate visualizations and summaries
  • Describe and employ appropriate sampling techniques
  • Analyze and critique elements of experimental design
  • Compute and interpret theoretical probabilities, including those derived from normal distributions
  • Evaluate and critique business case studies using the tools of Statistics
Topic #1: Descriptive Statistics

The Descriptive Statistics portion of this course sets the groundwork of what Statistics is and how we talk about it. The topic begins with an introduction to the basics of data and the vocabulary of Statistics before moving on to the numerous ways we can describe, display, and categorize Statistical data.

Topic #2: Randomization, Sampling, and Experiments

The second topic of this course dives into greater detail on some of the key tools we make use of in the field of Statistics. Learners will be introduced to the concept of randomness and the ramifications of it to the study of Statistics as well as the ways we can work with and control it when performing statistical calculations. This leads into a discussion of how we define experiments in the field of Statistics along with when and how to set them up.

Topic #3: Probability Theory

The final topic of our Statistics course is a discussion of probability theory, which is a foundational concept to the field. After we set the groundwork with the basics of probability and the tools we have to calculate it, students will learn how to identify random variables and perform calculations using them, before we conclude our discussion by introducing the Normal Model.