Next Generation Sunshine State Standards
MA.912.S.1: Learn to define appropriate questions for research, and to pose questions in a form that can be answered by collecting and analyzing data.
MA.912.S.1.1: Formulate an appropriate research question to be answered by collecting data or performing an experiment.
MA.912.S.1.2: Determine appropriate and consistent standards of measurement for the data to be collected in a survey or experiment.
MA.912.S.2: Learn key methods for collecting data and basic sampling principles.
MA.912.S.2.1: Compare the difference between surveys, experiments, and observational studies, and what types of questions can and cannot be answered by a particular design.
MA.912.S.2.2: Apply the definition of random sample and basic types of sampling, including representative samples, stratified samples, censuses.
MA.912.S.2.3: Identify sources of bias, including sampling and non-sampling errors.
MA.912.S.3: Learn to work with summary measures of sets of data, including measures of the center, spread, and strength of relationship between variables. Learn to distinguish between different types of data and to select the appropriate visual form to present different types of data.
MA.912.S.3.1: Read and interpret data presented in various formats. Determine whether data is presented in appropriate format, and identify possible corrections. Formats to include:
MA.912.S.3.1.a: bar graphs
MA.912.S.3.1.c: stem and leaf plots
MA.912.S.3.1.f: box and whiskers plots
MA.912.S.3.1.g: scatter plots
MA.912.S.3.1.h: cumulative frequency (ogive) graphs
MA.912.S.3.2: Collect, organize, and analyze data sets, determine the best format for the data and present visual summaries from the following:
MA.912.S.3.2.a: bar graphs
MA.912.S.3.2.c: stem and leaf plots
MA.912.S.3.2.f: box and whisker plots
MA.912.S.3.2.g: scatter plots
MA.912.S.3.2.h: cumulative frequency (ogive) graphs
MA.912.S.3.3: Calculate and interpret measures of the center of a set of data, including mean, median, and weighted mean, and use these measures to make comparisons among sets of data.
MA.912.S.3.4: Calculate and interpret measures of variance and standard deviation. Use these measures to make comparisons among sets of data.
MA.912.S.3.5: Calculate and interpret the range and quartiles of a set of data.
MA.912.S.3.6: Use empirical rules (e.g. 68-95-99.7 rule) to estimate spread of distributions and to make comparisons among sets of data.
MA.912.S.3.7: Calculate the correlation coefficient of a set of paired data, and interpret the coefficient as a measure of the strength and direction of the relationship between the variables.
MA.912.S.3.8: Determine whether a data distribution is symmetric or skewed based on an appropriate graphical presentation of the data.
MA.912.S.3.9: Identify outliers in a set of data based on an appropriate graphical presentation of the data, and describe the effect of outliers on the mean, median, and range of the data.
MA.912.S.4: Learn to use simulations of standard sampling distributions to determine confidence levels and margins of error. Develop measures of association between two numerical or categorical variables. Use technological tools to find equations of regression lines and correlation coefficients.
MA.912.S.4.1: Explain and interpret the concepts of confidence level and ?margin of error?.
MA.912.S.4.2: Use a simulation to approximate sampling distributions for the mean, using repeated sampling simulations from a given population.
MA.912.S.4.3: Apply the Central Limit Theorem to solve problems.
MA.912.S.4.4: Approximate confidence intervals for means using simulations of the distribution of the sample mean.
MA.912.S.4.5: Find the equation of the least squares regression line for a set of data.
MA.912.S.5: Gather data and determine confidence intervals to make inferences about means, and use hypothesis tests to make decisions. Learn to use data to approximate p-values and to determine whether correlations between variables are significant.
MA.912.S.5.1: Analyze the relationship between confidence level, margin of error and sample size.
MA.912.S.5.3: Explain and identify the following: null hypothesis, alternative hypotheses, Type I error, and Type II error.
MA.912.S.5.8: Use a regression line equation to make predictions.
MA.912.S.5.9: Interpret the coefficient of determination, r², for a least-squares regression.
Correlation last revised: 1/20/2017