Experience with descriptive statistics began as early as Grade 6. Students were expected to display numerical data and summarize it using measures of center and variability. By the end of middle school they were creating scatter plots and recognizing linear trends in data. This unit builds upon that prior experience, providing students with more formal means of assessing how a model fits data. Students use regression techniques to describe approximately linear relationships between quantities. They use graphical representations and knowledge of the context to make judgments about the appropriateness of linear models. With linear models, they look at residuals to analyze the goodness of fit.

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In this lesson, students will learn to choose a summary statistic appropriate to the characteristics of the data distribution, such as the shape of the distribution or the existence of extreme data points.
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In this lesson, students will take a more sophisticated look at using a linear function to model the relationship between two numerical variables. In addition to fitting a line to data, students assess how well the model fits by analyzing residuals.
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This lesson introduces the correlation coefficient. The focus is on the computation and interpretation of the correlation coefficient as a measure of how well data fits the relationship.
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In this lesson, students will learn more about differences in the shape, center and spread of normal distribution and probability. They choose a summary statistic appropriate to the characteristics of the data distribution, such as the shape of the distribution or the existence of extreme data points.
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In this project we apply what students learned in the first four lessons (3.1-3.4). Students will summarize, represent, and interpret data on single and multiple variables. The activities are designed to give students experience interpreting linear models. Students will calculate the mean, median, mode and range of data sets.
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