In Module 2, Topic D, students analyze relationships between two quantitative variables using scatterplots and by summarizing linear relationships using the least-squares regression line. Models are proposed based on an understanding of the equations representing the models and the observed pattern in the scatter plot. Students calculate and analyze residuals based on an interpretation of residuals as prediction errors.

Content Standard(s):

Mathematics MA2015 (2016) Grade: 9-12 Algebra I

45 ) Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. [S-ID6]

a. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models. [S-ID6a]

b. Informally assess the fit of a function by plotting and analyzing residuals. [S-ID6b]

c. Fit a linear function for a scatter plot that suggests a linear association. [S-ID6c]

Alabama Alternate Achievement Standards

AAS Standard: M.AAS.SP.HS.45- Given a scatter plot with data with a line of best fit that can be represented by a linear function, describe what is happening to the y-values in reference to the x-values (x- and y- values limited positive numbers).

Mathematics MA2015 (2016) Grade: 9-12 Algebra I

46 ) Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. [S-ID7]

Alabama Alternate Achievement Standards

AAS Standard: M.AAS.SP.HS.46- Given a graph that describes a set of linear data, identify the rate of change (slope) and constant term (y-intercept). (Use context of data—the total price of the stamps is calculated by increasing 50 cents for every stamp purchased or the cost if no stamps are purchased is $0.)

Tags:

constant term, correlation coefficient, data, function, intercept, linear function, quantitative variables, rate of change, residuals, scatter plot, slope, technology, variables