A.DSR.10Univariate and Bivariate Data

Expectations

• A.DSR.10.1

  Use statistics appropriate to the shape of the data distribution to compare and represent center (median and mean) and variability (interquartile range, standard deviation) of two or more distributions by hand and using technology.

• A.DSR.10.2

  Interpret differences in shape, center, and variability of the distributions based on the investigation, accounting for possible effects of extreme data points (outliers).

• A.DSR.10.3

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

• A.DSR.10.4

  Interpret the slope (predicted rate of change) and the intercept (constant term) of a linear model based on the investigation of the data.

• A.DSR.10.5

  Calculate the line of best fit and interpret the correlation coefficient, $r$, of a linear fit using technology. Use $r$ to describe the strength of the goodness of fit of the regression. Use the linear function to make predictions and assess how reasonable the prediction is in context.

• A.DSR.10.6

  Decide which type of function is most appropriate by observing graphed data.

• A.DSR.10.7

  Distinguish between correlation and causation.

Georgia Milestones coverage

• Algebra I (EOC) · Data & Statistical Reasoning

  Collect, analyze, and interpret univariate quantitative data to answer statistical investigative questions that compare groups to solve real-life problems; Represent bivariate data on a scatter plot and fit a function to the data to answer statistical questions and solve real-life problems.

  about 10% of the test · 6 points

What mastery looks like

Georgia's Achievement Level Descriptors. The levels are cumulative: each includes the ones before it. Proficient is on grade level.

Beginning

Find measures of center and/or variability of multiple data sets. Identify the strength of a given correlation coefficient. Determine when a linear model is the best model for a data set in two variables. Determine whether a set of data that shows correlation is an example of causation.

Developing

Compare measures of center or variability from two data sets. Identify outliers in a data set. Create a scatter plot and describe the relationship between the variables. Write the equation of a line of best fit and/or graph the line of best fit of a scatter plot and make predictions. Determine when an exponential or quadratic model is the best model for a data set in two variables. Explain how correlation is not deterministic of causation.

Proficient

Compare measures of center or variability from more than two data sets. Describe the impact of outliers on the shape, center, and variability of a data distribution. Interpret the slope and intercept of a line of best fit in the framework of the problem. Interpret the correlation coefficient for a set of data on a scatter plot.

Distinguished

Solve real-life problems by comparing multiple data sets in one variable on number lines, using measures of center and variability and outliers to describe the similarities and differences of the data sets. Solve real-life problems by comparing multiple data sets in two variables on scatter plots, using lines of best fit and correlation coefficients.