Here are problems that are similar to the ones you might see on the exam. Be sure to also review old quiz and workshop questions too. The exam will have both multiple choice and short answer questions. Answers to the odd problems are in the back of the book. If you click on the problem number in the book it should take you to the answer.
These are tricky. Be sure you understand how these work in two-way tables and when you are given the probabilties. Also, make sure you understand the difference between the probability of A and B both happening, versus the probability of A given B.
One way to keep the multiplication and addition rules straight is to make a weighted tree diagram.
Expected value is the weighted average of the outcomes in a probability model. Make sure you understand why it is called “expected” and how to calculate it. You should know the Law of Large Numbers too.
When we use a letter to represent the numerical outcome of a probability model, that letter is called a random variable. You should be comfortable with the way random variables are used in notation, and know how to find the expected value (also known as the theoretical average value) of a simple random variable.
You should be able to tell the difference between random variables that are continuous (like height and weight) and ones that are discrete (like number of siblings or the result of rolling some dice). A continuous variable can take any value in an interval between two points. A discrete variable can only take a finite number of values between any two points. For example the normal distribution is continuous but the binomial distribution is discrete.
Make sure you know the shape, center, and spread for the sampling distributions of the sample mean \(\bar{x}\) and the sample proportion \(\hat{p}\). Be sure you can describe how they change as the sample size gets larger.