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Random Experiments, Outcomes, and Sample Spaces
Represent uncertain processes with discrete or continuous sample spaces and carefully defined events.
34 min3 outcomes
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Module 5
Quantify uncertainty, calculate event probabilities, revise beliefs when new evidence arrives, and evaluate risk in business decisions.
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Lessons
16
Status
Available
Progress
0%
Represent uncertain processes with discrete or continuous sample spaces and carefully defined events.
Choose a probability interpretation that matches available evidence and the stability of the process.
Translate business language into complements, inclusive unions, intersections, and joint events.
Use general and special addition rules while distinguishing exclusivity from independence.
Restrict the sample space to a named condition and calculate with the correct denominator.
Connect conditional and joint probability and use the independence shortcut only when justified.
Convert between probability and clearly labelled odds while handling boundary cases.
Evaluate independence through conditional and multiplication checks without causal overreach.
Calculate redundant uptime and downtime while accounting for shared failure risks.
Turn a frequency table into validated marginal, joint, and conditional probabilities.
Compare conditional and marginal probabilities descriptively without introducing formal inference.
Organize conditional branches, terminal outcomes, and total probabilities in sequential processes.
Update a prior probability after observing a signal and explain the base-rate effect.
Update several possible causes using weighted likelihoods and connect posterior shares to decisions.
Count stage-by-stage configurations and ordered arrangements with validated nonnegative integers.
Select the correct counting rule, then integrate probability, Bayes, reliability, and counting in a retail-risk decision.
Probability practice library
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