View Standards
**Standard(s): **
[MA2015] AL1 (9-12) 41 :

[MA2015] AL1 (9-12) 45 :

41 ) Represent data with plots on the real number line (dot plots, histograms, and box plots). [S-ID1]

[MA2015] AL1 (9-12) 45 :

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]

These interactive tools give students an opportunity to explore more about stemplots, control charts, and histograms as covered in the Against All Odds statistics series. Simulations allow students to explore statistics methods in-depth using their own data.

View Standards
**Standard(s): **
[MA2015] AL1 (9-12) 45 :

[MA2015] AL1 (9-12) 46 :

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]

[MA2015] AL1 (9-12) 46 :

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

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.