Create plots of data on a real number line such as dot plots, histograms, and box plots. Utilize the appropriate statistical measures based on the data distribution to compare the center and spread of multiple data sets. Consider the potential effects of outliers when interpreting differences in the shape, center, and spread of data sets.

Use the mean and standard deviation to fit data to a normal distribution and estimate population percentages, knowing this may not be suitable for all data sets. Use tools like calculators, spreadsheets, or tables to calculate areas under the normal curve. Condense categorical data into two-way frequency tables and interpret relative frequencies in relation to the data, recognizing potential patterns and associations.

Utilize functions derived from data to address problems, choosing a function that aligns with the context and focusing on linear, quadratic, and exponential models. Assess the aptness of a function through plotting and analyzing residuals. Fit a linear function to a scatter plot that suggests a linear correlation.

Understand and interpret the slope and intercept in the context of the data. Calculate and interpret the correlation coefficient of a linear fit using technology. Also, understand the difference between correlation and causation.