Probability lec

Question 1

Result of an observation or experiment, or the descriptor or some potential outcome.

Answer 1

EVENT

Question 2

A subset of the set of all possible outcomes of a probabilistic experiment.

Answer 2

EVENT

Question 3

In probability studies, events are represented by

Answer 3

uppercase letters (A, B, etc.).

Question 4

The measure of the likelihood of an event happening.
* Measures the certainty of an event.

Answer 4

PROBABILITY

Question 5

The intersection of two events is read as

Answer 5

“both A and B”.

INTERSECTION (A∩B)

Question 6

. If either A and B are not fulfilled, this event, this intersection is not counted/valued for.

Answer 6

INTERSECTION (A∩B)

Question 7

The union of two events is read as

Answer 7

“Either A or B, or
both A and B”.

UNION (A∪B)

Question 8

The complement of an event is read as “Not A”. this means that the non-fulfillment or the converse/opposite event of A is the trigger for this operation.

Answer 8

COMPLEMENT (Ac or Ā)

Question 9

In the Venn diagram below, the area within the rectangle box represents all the outcomes

Answer 9

sample space

Question 10

Represents all the outcomes that could possibly occur,

Answer 10

sample space

Question 11

sample space symbol

Answer 11

P(S)=1

Question 12

An event that can never occur and has a probability of zero, whereas most events have probabilities between the value 0 and 1.

Answer 12

NULL EVENT

Question 13

NULL EVENT symbol

Answer 13

A∩Ac=∅

Question 14

When two events are mutually exclusive, the additive rule of probability states that the probability that either of the two events will occur is equal to the sum of the probabilities of the individual events.

Answer 14

ADDITIVE RULE OF PROBABILITY

Question 15

ADDITIVE RULE OF PROBABILITY formula

Answer 15

P(A∪B) = P(A) + P(B)

Question 16

This is the type of probability wherein the probability of one event is directly influenced by the probability of another event.

Answer 16

CONDITIONAL PROBABILITY

Question 17

CONDITIONAL PROBABILITY symbol

Answer 17

P(A|B)

Question 18

The multiplicative rule of probability states that the probability that two events A and B will both occur is equal to the probability of B multiplied by the conditional probability of A given that B has already occurred.

Answer 18

MULTIPLICATIVE RULE OF PROBABILITY

Question 19

MULTIPLICATIVE RULE OF PROBABILITY symbol

Answer 19

P(A∩B) = P(B) P(A|B)

P(A∩B) = P(A) P(B|A)

P(A∩B) = P(A) P(B)

Question 20

A fundamental rule relating marginal probabilities to conditional probabilities

Answer 20

TOTAL PROBABILITY

Question 21

It expresses the total probability of an outcome which can be realized via several distinct events.

Answer 21

TOTAL PROBABILITY

Question 22

set of events that all amount to the sum of 1 – meaning that there are no other possible outcomes, and it must fall under one of the categories.

Answer 22

exhaustive

Question 23

exhaustive symbol

Answer 23

S = A1∪A2∪A3

Question 24

This is a function wherein you want to compare the probability of occurrence of an event between two groups.

Answer 24

RELATIVE RISK (RR)

Question 25

This describes the probability or chance of a member of the exposed group to develop the outcome in relation to the probability of a membernof the unexposed group to develop the same outcome.

Answer 25

RELATIVE RISK (RR)

Question 26

formula for relative risk

Answer 26

P (outcome | exposed) / P(outcome |unexposed)

Question 27

f an event takes place with probability p, then the odds in favor of the event are the probability that the event will occur divided by the probability the event will not occur or ___

Answer 27

odds ratio, p/(1-p) to 1.

Question 28

This is a function wherein you want to calculate for the odds in favor of the event.

Answer 28

odds ratio

Question 29

The ___ represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

Answer 29

The OR

Question 30

formula for odd

Answer 30

P (outcome | exposed)/[1-P(outcome |exposed)] over P (outcome | unexposed)/[1-P(outcome |unexposed)]