Maryland College and Career-Ready Standards: Statistics
Skills available for Maryland Statistics math standards
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S
Statistics and Probability
S.ID Interpreting Categorical and Quantitative Data
S.ID.A Summarize, represent, and interpret data on a single count or measurement variable.
S.ID.A Summarize, represent, and interpret data on a single count or measurement variable.
S.ID.A.1: Represent data with plots on the real number line (dot plots, histograms and box plots).
S.ID.A.1: Represent data with plots on the real number line (dot plots, histograms and box plots).
S.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.
S.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.
S.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).
S.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).
S.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, spreadsheets, and tables to estimate areas under the normal curve.
S.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, spreadsheets, and tables to estimate areas under the normal curve.
S.ID.B Summarize, represent, and interpret data on two categorical and quantitative variables.
S.ID.B Summarize, represent, and interpret data on two categorical and quantitative variables.
S.ID.B.5: 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.
S.ID.B.5: 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.
S.ID.B.6: Represent data on two quantitative variables on a scatter plot and describe how the variables are related.
S.ID.B.6: Represent data on two quantitative variables on a scatter plot and describe how the variables are related.
S.ID.B.6.a: Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
S.ID.B.6.a: Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
S.ID.B.6.b: Informally assess the fit of a function by plotting and analyzing residuals.
S.ID.B.6.b: Informally assess the fit of a function by plotting and analyzing residuals.
S.ID.B.6.c: Fit a linear function for a scatter plot that suggests a linear association.
S.ID.B.6.c: Fit a linear function for a scatter plot that suggests a linear association.
S.ID.C Interpret linear models.
S.ID.C Interpret linear models.
S.ID.C.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
S.ID.C.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
S.ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.
S.ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.
S.ID.C.9: Distinguish between correlation and causation.
S.ID.C.9: Distinguish between correlation and causation.
S.IC Making Inferences and Justifying Conclusions
S.IC.A Understand and evaluate random processes underlying statistical experiments
S.IC.A Understand and evaluate random processes underlying statistical experiments
S.IC.A.1: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
S.IC.A.1: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
S.IC.A.1.a: Introduce sampling distributions as a means to explore variability in sample statistics and ultimately evaluate a claim about a population.
S.IC.A.1.a: Introduce sampling distributions as a means to explore variability in sample statistics and ultimately evaluate a claim about a population.
S.IC.A.2: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
S.IC.A.2: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
S.IC.B Make inferences and justify conclusions from sample surveys, experiments and observational studies.
S.IC.B Make inferences and justify conclusions from sample surveys, experiments and observational studies.
S.IC.B.3: Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relate to each.
S.IC.B.3: Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relate to each.
S.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.
S.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.
S.IC.B.5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
S.IC.B.5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
S.IC.B.6: Evaluate reports based on data.
S.IC.B.6: Evaluate reports based on data.
S.IC.B.7: Conduct statistical investigations.
S.IC.B.7: Conduct statistical investigations.
S.IC.B.7.a: Conduct observational studies.
S.IC.B.7.a: Conduct observational studies.
S.IC.B.7.b: Conduct statistical experiments.
S.IC.B.7.b: Conduct statistical experiments.
S.CP Conditional Probability and the Rules of Probability
S.CP.A Understand independence and conditional probability and use them to interpret data.
S.CP.A Understand independence and conditional probability and use them to interpret data.
S.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").
S.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").
S.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.
S.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.
S.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.
S.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.
S.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.
S.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.
S.CP.A.5: Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
S.CP.A.5: Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
S.CP.B Use the rules of probability to compute probabilities of compound events.
S.CP.B Use the rules of probability to compute probabilities of compound events.
S.CP.B.6: 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.6: 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: 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.7: 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: Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B|A) = P(B)P(B|A), and interpret the answer in terms of the model.
S.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(B|A), and interpret the answer in terms of the model.
S.CP.B.9: Use permutations and combinations to compute probabilities of compound events and solve problems.
S.CP.B.9: Use permutations and combinations to compute probabilities of compound events and solve problems.
S.MD Using Probability to Make Decisions
S.MD.A Calculate expected values and use them to solve problems.
S.MD.A Calculate expected values and use them to solve problems.
S.MD.A.1: 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.1: 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: Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
S.MD.A.2: Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
S.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.
S.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.
S.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.
S.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.
S.MD.B Use probability to evaluate outcomes of decisions.
S.MD.B Use probability to evaluate outcomes of decisions.
S.MD.B.5: Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values.
S.MD.B.5: Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values.
S.MD.B.5.a: Find the expected payoff for a game of chance.
S.MD.B.5.a: Find the expected payoff for a game of chance.
S.MD.B.5.b: Evaluate and compare strategies based on expected values.
S.MD.B.5.b: Evaluate and compare strategies based on expected values.
S.MD.B.6: Use probabilities to make fair decisions.
S.MD.B.6: Use probabilities to make fair decisions.
S.MD.B.7: Analyze decisions and strategies using probability concepts.
S.MD.B.7: Analyze decisions and strategies using probability concepts.