An introduction to probability, with the aim of developing probabilistic intuition as well as techniques needed to analyze simple random samples.
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Statistics 2 at Berkeley is an introductory class taken by about 1000 students each year. Stat2.2x is the second of three five-week courses that make up Stat2x, the online equivalent of Berkeley's Stat 2.
The focus of Stat2.2x is on probability theory: exactly what is a random sample, and how does randomness work? If you buy 10 lottery tickets instead of 1, does your chance of winning go up by a factor of 10? What is the law of averages? How can polls make accurate predictions based on data from small fractions of the population? What should you expect to happen "just by chance"? These are some of the questions we will address in the course.
We will start with exact calculations of chances when the experiments are small enough that exact calculations are feasible and interesting. Then we will step back from all the details and try to identify features of large random samples that will help us approximate probabilities that are hard to compute exactly. We will study sums and averages of large random samples, discuss the factors that affect their accuracy, and use the normal approximation for their probability distributions.
Be warned: by the end of Stat2.2x you will not want to gamble. Ever. (Unless you're really good at counting cards, in which case you could try blackjack, but perhaps after taking all these edX courses you'll find other ways of earning money.)
As Stat2.2x is part of a series, the basic prerequisites are the same as those for Stat2.1x: high school arithmetic and good comprehension of English. In addition, you are expected to know the material of Stat2.1x, with particular emphasis on histograms, averages, SDs, and the normal curve. The fundamental approach of the series was provided in the description of Stat2.1x and appears here again: There will be no mindless memorization of formulas and methods. Throughout the course, the emphasis will be on understanding the reasoning behind the calculations, the assumptions under which they are valid, and the correct interpretation of results.
