High School: Statistics & Probability » Interpreting Categorical & Quantitative Data

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Standards in this domain:

Summarize, represent, and interpret data on a single count or measurement variable

CCSS.Math.Content.HSS.ID.A.1
Represent data with plots on the real number line (dot plots, histograms, and box plots).
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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.
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Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
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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.

Summarize, represent, and interpret data on two categorical and quantitative variables

CCSS.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.
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Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
CCSS.Math.Content.HSS.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.
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Informally assess the fit of a function by plotting and analyzing residuals.
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Fit a linear function for a scatter plot that suggests a linear association.

Interpret linear models

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Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
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Compute (using technology) and interpret the correlation coefficient of a linear fit.
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Distinguish between correlation and causation.