allenai/achieve-the-core huggingface dataset:
S-CP.A.1 (Standard, none): Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events (“or,” “and,” “not”).
S-CP.A.2 (Standard, none): 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.
S-CP.A.3 (Standard, none): 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.
S-CP.A.4 (Standard, none): 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.
S-CP.A.5 (Standard, none): 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.
S-CP.B (Cluster, none): Use the rules of probability to compute probabilities of compound events in a uniform probability model.
S-CP.B.6 (Standard, none): 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.
S-CP.B.7 (Standard, none): 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.
S-CP.B.8 (Standard, none): 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.
S-CP.B.9 (Standard, none): Use permutations and combinations to compute probabilities of compound events and solve problems.
S-MD.A (Cluster, none): Calculate expected values and use them to solve problems.
S-MD.A.1 (Standard, none): Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions.
S-MD.A.2 (Standard, none): Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
S-MD.A.3 (Standard, none): Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value. For example, find the theoretical probability distribution for the number of correct answers obtained by guessing on all five questions of a multiple-choice test where each question has four choices, and find the expected grade under various grading schemes.
S-MD.A.4 (Standard, none): Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value. For example, find a current data distribution on the number of TV sets per household in the United States, and calculate the expected number of sets per household. How many TV sets would you expect to find in 100 randomly selected households?
S-MD.B.5 (Standard, none): Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values.
S-MD.B.5a (Sub-standard, none): Find the expected payoff for a game of chance. For example, find the expected winnings from a state lottery ticket or a game at a fast food restaurant.
S-MD.B.5b (Sub-standard, none): Evaluate and compare strategies on the basis of expected values. For example, compare a high-deductible versus a low-deductible automobile insurance policy using various, but reasonable, chances of having a minor or a major accident.
S-MD.B.6 (Standard, none): Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator).
S-MD.B.7 (Standard, none): Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
3.NF (Domain, none): Number & Operations - Fractions: Grade 3 expectations in this domain are limited to fractions with denominators 2, 3, 4, 6, and 8.
4.NBT (Domain, none): Number & Operations in Base Ten: Grade 4 expectations in this domain are limited to whole numbers less than or equal to 1,000,000.
4.NF (Domain, none): Number & Operations - Fractions: Grade 4 expectations in this domain are limited to fractions with denominators 2, 3, 4, 5, 6, 8, 10, 12, and 100.
A-APR (Domain, none): Arithmetic with Polynomials & Rational Expressions
G-SRT (Domain, none): Similarity, Right Triangles, & Trigonometry