All Lessons

(B) Determine the image or pre-image of a given two-dimensional figure under a composition of rigid transformations, a composition of non-rigid transformations, and a composition of both, including dilations where the center can be any point in the plane

(C) Apply the definition of congruence, in terms of rigid transformations, to identify congruent figures and their corresponding sides and angles

(Y8) Define congruence of plane shapes using transformations (ACMMG200)

8.G.2 Understand that a two-dimensional figure is congruent to another if the second can be obtained from the first by a sequence of rotations, reflections, and translations; given two congruent figures, describe a sequence that exhibits the congruence between them.

HSG.CO.6 Use geometric descriptions of rigid motions to transform figures and to predict the effect of a given rigid motion on a given figure; given two figures, use the definition of congruence in terms of rigid motions to decide if they are congruent.

(B) Prove two triangles are congruent by applying the Side-Angle-Side, Angle-Side-Angle, Side-Side-Side, Angle-Angle-Side, and Hypotenuse-Leg congruence conditions

(Y8) Develop the conditions for congruence of triangles (ACMMG201)

HSG.CO.7 Use the definition of congruence in terms of rigid motions to show that two triangles are congruent if and only if corresponding pairs of sides and corresponding pairs of angles are congruent.

(A) Distinguish between categorical and quantitative data

(Y11-12) Identify examples of categorical data (ACMEM043)

(Y11-12) Identify examples of numerical data (ACMEM044)

HSS.IDA.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.

(F) Analyze categorical data, including determining marginal and conditional distributions, using two-way tables.

(Y11-12) Interpret information presented in two-way tables (ACMEM038)

HSS.IDA.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.

(F) Analyze categorical data, including determining marginal and conditional distributions, using two-way tables.

(Y11-12) Interpret information presented in two-way tables (ACMEM038)

HSS.IDA.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.

(A) Construct a scatterplot and describe the observed data to address questions of association such as linear, non-linear, and no association between bivariate data

(Y11-12) Describe the patterns and features of bivariate data (ACMEM138)

(Y11-12) Describe the association between two numerical variables in terms of direction (positive/negative), form (linear/non-linear) and strength (strong/moderate/weak) (ACMEM139)

HSS.IDA.6 Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.

(A) Calculate, using technology, the correlation coefficient between two quantitative variables and interpret this quantity as a measure of the strength of the linear association

(Y11-12) Describe the patterns and features of bivariate data (ACMEM138)

(Y11-12) Describe the association between two numerical variables in terms of direction (positive/negative), form (linear/non-linear) and strength (strong/moderate/weak) (ACMEM139)

HSS.IDA.6 Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.

(B) Use regression methods available through technology to write a linear function, a quadratic function, and an exponential function from a given set of data

(Y11-12) Use technology to find the line of best fit (ACMEM142)

HSS.IDA.6.C Fit a linear function for a scatter plot that suggests a linear association.