Download Ch5-7 Vocabulary (with definitions)

MTH243 – Probability and Statistics – T. Davenport
Ch5 – Vocabulary
Sec 5.1: Probability Rules
The Law of Large Numbers (p. 255) – As the number of repetitions of a probability experiment
increases, the proportion with which a certain outcome is observed gets closer to the probability of the
outcome.
Experiment (p. 256) – any process with uncertain results that can be repeated.
Sample space, S (p. 256) – the collection of all possible outcomes of a probability experiment.
Event (p. 256) – any collection of outcomes from a probability experiment. An event consists of one
outcome or more than one outcome. We will denote events with one outcome (simple events) ei. In
general, events are denoted using capital letters such as E.
Probability model (p. 257) – lists the possible outcomes of a probability experiment and each
outcome’s probability. If an event is impossible, the probability of the event is 0. If an event is
certainty, the probability of the event is 1.
Unusual event (p. 257) – an event that has a low probability of occurring. [typically < 0.05 (or 5%), but
not set in stone]
Empirical evidence (p. 258) – evidence based on the outcomes of a probability experiment.
Equally likely outcomes (p. 259) – each outcome has the same probability of occurring.
Tree diagram (p. 261) – list the equally likely outcomes of the experiment using a branch for each
possible outcome.
Subjective probability (p. 264) – a probability obtained on the basis of personal judgment.
Sec 5.2: The Addition Rule and Complements
Disjoint (p. 269) – two events that have no outcomes in common. (Also called mutually exclusive.)
Venn diagrams (p. 269) – represent events as circles enclosed in a rectangle that represents the
sample space.
OR:
Complement of an event (p. 274) – Let S denote the sample space and let E denote an event. The
complement of E, denoted EC, is all outcomes in the sample space S that are not outcomes in the event
E.
Sec 5.3: Independence and the Multiplication Rule
Independent (p. 281) – two events E and F are independent if the occurrence of event E in a
probability experiment does not affect the probability of event F.
Dependent (p. 281) – two events are dependent if the occurrence of event E in a probability
experiment affects the probability of event F.
AND:
SUMMARY:
Flowchart:
MTH243 – Probability and Statistics – T. Davenport
Ch6 – Vocabulary
Sec 6.1: Discrete Random Variables
Sec 6.2: The Binomial Probability Distribution
MTH243 – Probability and Statistics – T. Davenport
Ch7 – Vocabulary
Sec 7.1: Properties of the Normal Distribution
Sec 7.2: Applications of the Normal Distribution