Mathematics | Course : Model Mathematics II (Integrated Pathway)
Domain - Conditional Probability and the Rules of Probability
Cluster - Understand independence and conditional probability and use them to interpret data from simulations or experiments.
[MII.S-CP.A.5] - Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.* For example, compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.
- Probability
A number between 0 and 1 used to quantify likelihood for processes that have uncertain outcomes (such as tossing a coin, selecting a person at random from a group of people, tossing a ball at a target, testing for a medical condition).
[MII.S-CP.A.2] -
Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent.*
[MII.S-CP.A.3] -
Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B.*
[MII.S-CP.A.4] -
Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities.* For example, collect data from a random sample of students in your school on their favorite subject among math, science, and English. Estimate the probability that a randomly selected student from your school will favor science given that the student is in tenth grade. Do the same for other subjects and compare the results.
[MII.S-CP.B.6] -
Find the conditional probability of A given B as the fraction of B’s outcomes that also belong to A, and interpret the answer in terms of the model.*
[HS.LS.3.3] -
Apply concepts of probability to represent possible genotype and phenotype combinations in offspring caused by different types of Mendelian inheritance patterns. Clarification Statements: Representations can include Punnett squares, diagrams, pedigree charts, and simulations. Inheritance patterns include dominant-recessive, codominance, incomplete dominance, and sex-linked.