AR.Math.Content.HSS.ID.A.1: Represent data with plots on the real number line (dot plots, histograms, and box plots).
AR.Math.Content.HSS.ID.A.2: 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.
AR.Math.Content.HSS.ID.A.3: Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
AR.Math.Content.HSS.ID.A.4: 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 and/or spreadsheets to estimate areas under the normal curve.
AR.Math.Content.HSS.ID.B.6: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Informally assess the fit of a function by plotting and analyzing residuals.
AR.Math.Content.HSS.ID.C.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
AR.Math.Content.HSS.ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.
AR.Math.Content.HSS.IC.A.1: Recognize statistics as a process for making inferences about population parameters based on a random sample from that population.
AR.Math.Content.HSS.IC.A.2: Compare theoretical and empirical probabilities using simulations (e.g. such as flipping a coin, rolling a number cube, spinning a spinner, and technology).
AR.Math.Content.HSS.IC.B.3: Recognize the purposes of and differences among sample surveys, experiments, and observational studies. Explain how randomization relates to sample surveys, experiments, and observational studies.
AR.Math.Content.HSS.IC.B.4: Use data from a sample survey to estimate a population mean or proportion. Develop a margin of error through the use of simulation models for random sampling.
AR.Math.Content.HSS.IC.B.5: Use data from a randomized experiment to compare two treatments. Use simulations to decide if differences between parameters are significant.
AR.Math.Content.HSS.IC.B.6: Read and explain, in context, the validity of data from outside reports by: Identifying the variables as quantitative or categorical. Describing how the data was collected. Indicating any potential biases or flaws. Identifying inferences the author of the report made from sample data.
AR.Math.Content.HSS.CP.A.1: 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”).
AR.Math.Content.HSS.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.
AR.Math.Content.HSS.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.
AR.Math.Content.HSS.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. 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.
AR.Math.Content.HSS.CP.B.6: Find the conditional probability of A given B.
AR.Math.Content.HSS.CP.B.8: 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.
AR.Math.Content.HSS.CP.B.9: Use permutations and combinations to compute probabilities of compound events and solve problems.
AR.Math.Content.HSS.CP.B.10: Use visual representations in counting (e.g. combinations, permutations, etc.) including but not limited to: Venn diagrams, Tree diagrams.
AR.Math.Content.HSS.MD.A.2: Calculate the expected value of a random variable. Interpret the expected value of a random variable as the mean of the probability distribution.
AR.Math.Content.HSS.MD.A.3: Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated. Find the expected value.
AR.Math.Content.HSS.MD.A.4: Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically. Find the expected value.
AR.Math.Content.HSS.MD.B.5: Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values. Find the expected payoff for a game of chance. Evaluate and compare strategies on the basis of expected values.
AR.Math.Content.HSS.MD.B.6: Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator).
AR.Math.Content.HSS.MD.B.7: Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
Correlation last revised: 9/16/2020