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Learning Outcome Statements PDF Download
define a random variable, an outcome, and an event;
identify the two defining properties of probability, including mutually exclusive and exhaustive events, and compare and contrast empirical, subjective, and priori probabilities;
describe the probability of an event in terms of odds for and against the event;
|2. Unconditional, Conditional, and Joint Probabilities|
calculate and interpret conditional probabilities;
|3. Addition Rule for Probabilities: the Probability that at Least One of Two Events Will Occur|
demonstrate the application of the multiplication and addition rules for probability;
|4. Multiplication Rule for Independent Events|
compare and contrast dependent and independent events;
|5. The Total Probability Rule|
calculate and interpret an unconditional probability using the total probability rule;
|6. Expected Value (Mean), Variance, and Conditional Measures of Expected Value and Variance|
calculate and interpret the expected value, variance, and standard deviation of random variables;
explain the use of conditional expectation in investment applications;
interpret a probability tree and demonstrate its application to investment problems;
|7. Expected Value, Variance, Standard Deviation, Covariances, and Correlations of Portfolio Returns|
calculate and interpret the expected value, variance, standard deviation, covariances, and correlations of portfolio returns;
|8. Covariance Given a Joint Probability Function|
calculate and interpret the covariances of portfolio returns using the joint probability function;
|9. Bayes' Formula|
calculate and interpret an updated probability using Bayes' formula;
|10. Principles of Counting|
identify the most appropriate method to solve a particular counting problem and analyze counting problems using factorial, combination, and permutation concepts.
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