Categorical and Quantitative Data
CCR.Math.Content.HSS-ID.A.1
Represent data with plots on the real number line (dot plots, histograms, and box plots).
CCR.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.
CCR.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).
CCR.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, spreadsheets, and tables to estimate areas under the normal curve.
CCR.Math.Content.HSS-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.
CCR.Math.Content.HSS-ID.B.6
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
CCR.Math.Content.HSS-ID.B.6a
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.
CCR.Math.Content.HSS-ID.B.6b
Informally assess the fit of a function by plotting and analyzing residuals.
CCR.Math.Content.HSS-ID.B.6c
Fit a linear function for a scatter plot that suggests a linear association.
CCR.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.
CCR.Math.Content.HSS-ID.C.8
Compute (using technology) and interpret the correlation coefficient of a linear fit.
CCR.Math.Content.HSS-ID.C.9
Distinguish between correlation and causation.