Common Core State
HSS.CP.B6
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.
October 1, 2018HSS.CP.B7
Apply the Addition Rule, P(A or B) = P(A) + P(B) – P(A and B), and interpret the answer in terms of the model.
October 1, 2018HSS.CP.B8
(+) Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B|A) = P(B)P(A|B), and interpret the answer in terms of the model.
October 1, 2018HSN.VM.B5a
Represent scalar multiplication graphically by scaling vectors and possibly reversing their direction; perform scalar multiplication component-wise, e.g., as c(vx, vy) = (cvx, cvy).
October 1, 2018HSS.CP.B9
(+) Use permutations and combinations to compute probabilities of compound events and solve problems.
October 1, 2018HSN.VM.B5b
Compute the magnitude of a scalar multiple cv using ||cv|| = |c|v. Compute the direction of cv knowing that when |c|v ≠ 0, the direction of cv is either along v (for c > 0) or against v (for c < 0).
October 1, 2018HSS.IC.A1
Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
October 1, 2018HSN.VM.C6
(+) Use matrices to represent and manipulate data, e.g., to represent payoffs or incidence relationships in a network.
October 1, 2018HSS.IC.A2
Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. For example, a model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model?
October 1, 2018HSN.VM.C7
(+) Multiply matrices by scalars to produce new matrices, e.g., as when all of the payoffs in a game are doubled.
October 1, 2018HSN.VM.C9
(+) Understand that, unlike multiplication of numbers, matrix multiplication for square matrices is not a commutative operation, but still satisfies the associative and distributive properties.
October 1, 2018