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Mathematics | Course : Model Mathematics III (Integrated Pathway)
Domain - Making Inferences and Justifying Conclusions
Cluster - Understand and evaluate random processes underlying statistical experiments.
[MIII.S-IC.A.2] - 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 five tails in a row cause you to question the model?
- Model
A mathematical representation (e.g., number, graph, matrix, equation(s), geometric figure) for real-world or mathematical objects, properties, actions, or relationships.
[7.SP.C.6] -
Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.
[7.SP.C.7] -
Develop a probability model and use it to find probabilities of events. Compare probabilities from a model to observed frequencies; if the agreement is not good, explain possible sources of the discrepancy.
[7.SP.C.7.a] -
Develop a uniform probability model by assigning equal probability to all outcomes, and use the model to determine probabilities of events. For example, if a student is selected at random from a class, find the probability that Jane will be selected and the probability that a girl will be selected.
[7.SP.C.7.b] -
Develop a probability model (which may not be uniform) by observing frequencies in data generated from a chance process. For example, find the approximate probability that a spinning penny will land heads up or that a tossed paper cup will land open-end down. Do the outcomes for the spinning penny appear to be equally likely based on the observed frequencies.