Standards Map

Mathematics > Course Model Algebra I (Traditional Pathway) > Interpreting Categorical and Quantitative Data
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Mathematics | Course : Model Algebra I (Traditional Pathway)

Domain - Interpreting Categorical and Quantitative Data

Cluster - Interpret linear models.

[AI.S-ID.C.9] - Distinguish between correlation and causation.

Resources:

    Predecessor Standards:

    • 8.SP.A.1
      Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
    • 8.SP.A.4
      Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?

    Successor Standards:

    No Successor Standards found.

    Same Level Standards:

    • AI.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.*
    • AII.S-IC.B.6
      Evaluate reports based on data.*
    • HS.ETS.1.3
      Evaluate a solution to a complex real-world problem based on prioritized criteria and trade-offs that account for a range of constraints, including cost, safety, reliability, aesthetics, and maintenance, as well as social, cultural, and environmental impacts.*