Google Slide Presentations:

Unit Objectives:

• Choose a representation that best illustrates data in terms of context.
• Compare the center and spread of data sets using statistical displays appropriate to the shape of the data distributions.
• Interpret differences in shape, center, and spread in the context of data sets.
• Draw a line of best fit through a scatter plot by hand and using technology.
• Assess the fit of a function by calculating residuals.
• Determine the equation of a line of best fit and interpret the meaning of slope and y-intercept in context.
• Calculate and interpret the correlation of a line using r.
• Understand that correlation does not imply causation.
• Use the line of best fit to solve problems within the constraints of the data set.
• Understand how data is organized in a two-way table.
• Construct a two-way table and interpret the table to draw conclusions.
• Calculate joint, marginal, and conditional relative frequencies.

New Jersey Student Learning Standards for this Unit:

• S-ID.A.1: Represent data with plots on the real number line (dot plots, histograms, and box plots).
• S-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.
• S-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).
• S-ID.B.6b: Informally assess the fit of a function by plotting and analyzing residuals.
• S-ID.B.6c: Fit a linear function for a scatter plot that suggests a linear association.
• S-ID.C.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
• S-ID.C.8: Compute (using technology) and interpret the correlation coefficient of a linear fit.
• S-ID.C.9: Distinguish between correlation and causation.
• S.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.