edX
edXProbability and Statistics II: Random Variables – Great Expectations to Bell Curves
220 studentsINTERMEDIATEEnglish
Korshub's take
Random Variables: Foundation for Statistics
Probability springs to life through random variables. This course builds from discrete cases (customer arrivals, counts) to continuous ones (lifetimes, measurements), then explores properties like expected value, variance, and moment-generating functions. The abstract becomes concrete when applied to simulation and prediction.
You'll handle joint random variables, extract marginal and conditional information, and study independence and correlation. Hands-on R work grounds the theory. By course's end, you'll have the machinery for modeling and inference work ahead in applied statistics.
About this course
This course discusses properties and applications of random variables. When you’re done, you’ll have enough firepower to undertake a wide variety of modeling and analysis problems; and you’ll be well-prepared for the upcoming Statistics courses.
We’ll begin by introducing the concepts of discrete and continuous random variables. For instance, how many customers are likely to arrive in the next hour (discrete)? What’s the probability that a lightbulb will last more than a year (continuous)?
We’ll learn about various properties of random variables such as the expected value, variance, and moment generating function. This will lead us to a discussion of functions of random variables. Such functions have many uses, including some wonderful applications in computer simulations.
If you enjoy random variables, then you’ll really love joint (two-dimensional) random variables. We’ll provide methodology to extract marginal (one-dimensional) and conditional information from these big boys. This work will enable us to
study the important concepts of independence and correlation.
Along the way, we’ll start working with the R statistical package to do some of our calculations and analysis.
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