South Carolina flagSouth Carolina: Statistical Modeling Math Standards

29 standards · 1 domains

DATA, PROBABILITY, AND STATISTICAL REASONING

  • SM.DPSR.1.1 Calculate and interpret z-scores as a measure of relative standing to standardize units.
  • SM.DPSR.1.2 Approximate percentages using the Empirical Rule and z-scores for normally distributed data.
  • SM.DPSR.1.3 Using simulations taken from a given population, model sample-to-sample variability in sampling distributions of a statistic.
  • SM.DPSR.1.4 Construct and compare confidence intervals of different models to make conclusions about reliability given a margin of error.
  • SM.DPSR.1.5 Summarize and evaluate reports based on data for appropriateness of study design, analysis methods, and statistical measures used.
  • SM.DPSR.2.1 Formulate investigative statistical questions about a population using samples taken from the population.
  • SM.DPSR.2.2 Formulate comparative and associative investigative statistical questions for surveys and observational studies to compare two or more groups or to investigate the association of two or more variables.
  • SM.DPSR.2.3 Formulate comparative and associative investigative statistical questions for experiments to compare two or more groups or to investigate the association of two or more variables.
  • SM.DPSR.2.4 Formulate inferential investigative statistical questions regarding association and prediction.
  • SM.DPSR.2.5 Formulate investigative statistical questions for two variables.
  • SM.DPSR.3.1 Apply an appropriate data-collection plan when collecting data for the investigative statistical question of interest.
  • SM.DPSR.3.2 Distinguish between sample surveys, observational studies, and experiments.
  • SM.DPSR.3.3 Design sample surveys, experiments, and observational studies using statistical methods.
  • SM.DPSR.3.4 Differentiate between random selection and random assignment and identify their impact on generalizing.
  • SM.DPSR.3.5 Examine potential sources and effects of bias and confounding variables.
  • SM.DPSR.3.6 Describe and comply with the ethical use of data.
  • SM.DPSR.3.7 Identify when data can be generalized to a target population.
  • SM.DPSR.4.1 Describe quantitative and categorical data.
  • SM.DPSR.4.2 Summarize and describe relationships between two variables.
  • SM.DPSR.4.3 Describe the relationship between two quantitative variables by interpreting correlation (r) and a least-square regression line (using technology).
  • SM.DPSR.4.4 Assess the fit of a linear model by plotting and analyzing residuals, including the squares of the residuals, to improve its fit.
  • SM.DPSR.4.5 Calculate and interpret the p-value for a population proportion and/or population mean.
  • SM.DPSR.4.6 Use simulated sampling distributions to describe the sample-to-sample variability of sample statistics.
  • SM.DPSR.4.7 Use simulations to investigate associations between two categorical variables and to compare groups.
  • SM.DPSR.5.1 Use statistical evidence from analyses to answer investigative statistical questions.
  • SM.DPSR.5.2 Determine the possible impact of extreme data points, missing values, or incorrect values on the results.
  • SM.DPSR.5.3 Use and interpret the p-value to determine whether the estimate for a population parameter is reasonable.
  • SM.DPSR.5.4 Interpret a given margin of error corresponding to an estimate of a population parameter.
  • SM.DPSR.5.5 Explain the impact of multiple variables on one another.