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Skills available for Alabama Geometry standards

Standards are in black and IXL math skills are in dark green. Hold your mouse over the name of a skill to view a sample question. Click on the name of a skill to practice that skill.

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GDA.NQ Number and Quantity

GDA.AF Algebra and Functions

  • Focus 1: Algebra

    • The structure of an equation or inequality (including, but not limited to, one-variable linear and quadratic equations, inequalities, and systems of linear equations in two variables) can be purposefully analyzed (with and without technology) to determine an efficient strategy to find a solution, if one exists, and then to justify the solution.

      • 3 Find the coordinates of the vertices of a polygon determined by a set of lines, given their equations, by setting their function rules equal and solving, or by using their graphs.

    • Expressions, equations, and inequalities can be used to analyze and make predictions, both within mathematics and as mathematics is applied in different contexts – in particular, contexts that arise in relation to linear, quadratic, and exponential situations.

  • Focus 2: Connecting Algebra to Functions

    • Graphs can be used to obtain exact or approximate solutions of equations, inequalities, and systems of equations and inequalities—including systems of linear equations in two variables and systems of linear and quadratic equations (given or obtained by using technology).

GDA.DSP Data Analysis, Statistics, and Probability

  • Focus 1: Quantitative Literacy

    • Mathematical and statistical reasoning about data can be used to evaluate conclusions and assess risks.

  • Focus 2: Visualizing and Summarizing Data

    • Data arise from a context and come in two types: quantitative (continuous or discrete) and categorical. Technology can be used to "clean" and organize data, including very large data sets, into a useful and manageable structure – a first step in any analysis of data

      • 8 Use technology to organize data, including very large data sets, into a useful and manageable structure.

    • Distributions of quantitative data (continuous or discrete) in one variable should be described in the context of the data with respect to what is typical (the shape, with appropriate measures of center and variability, including standard deviation) and what is not (outliers), and these characteristics can be used to compare two or more subgroups with respect to a variable.

    • Scatter plots, including plots over time, can reveal patterns, trends, clusters, and gaps that are useful in analyzing the association between two contextual variables.

      • 12 Represent data of two quantitative variables on a scatter plot, and describe how the variables are related.

        • 12.a Find a linear function for a scatter plot that suggests a linear association and informally assess its fit by plotting and analyzing residuals, including the squares of the residuals, in order to improve its fit.

        • 12.b Use technology to find the least-squares line of best fit for two quantitative variables.

    • Analyzing the association between two quantitative variables should involve statistical procedures, such as examining (with technology) the sum of squared deviations in fitting a linear model, analyzing residuals for patterns, generating a least-squares regression line and finding a correlation coefficient, and differentiating between correlation and causation.

    • Data analysis techniques can be used to develop models of contextual situations and to generate and evaluate possible solutions to real problems involving those contexts.

      • 15 Evaluate possible solutions to real-life problems by developing linear models of contextual situations and using them to predict unknown values.

      • Checkpoint opportunity

GDA.GM Geometry and Measurement