Common Core: MATH.CONTENT
HSS.IC.B5
Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
October 1, 2018HSS.ID.A1
Represent data with plots on the real number line (dot plots, histograms, and box plots).
October 1, 2018HSS.ID.A2
Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
October 1, 2018HSS.ID.A3
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
October 1, 2018HSS.ID.A4
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
October 1, 2018HSS.ID.B5
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
October 1, 2018HSS.CP.A5
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.
October 1, 2018HSS.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, 2018