Interpreting Categorical and Quantitative Data
|Summarize, represent, and interpret data on a single count or measurement variable.|
|1. Represent data with plots on the real number line (dot plots, histograms, and box plots).|
|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.
|3. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme
data points (outliers).
|Summarize, represent, and interpret data on two categorical and quantitative variables.
[Linear focus; discuss general principle.]
|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
|6. Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.|
|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.
|b. Informally assess the fit of a function by plotting and analyzing residuals.|
|c. Fit a linear function for a scatter plot that suggests a linear association.|
|Interpret linear models.|
|7. Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.|
|8. Compute (using technology) and interpret the correlation coefficient of a linear fit.|
|9. Distinguish between correlation and causation.|