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Compute the Variability of Data Sets

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Compute the Variability of Data Sets

TEKS Math: 6.12.C

(C) Summarize numeric data with numerical summaries, including the mean and median (measures of center) and the range and interquartile range (IQR) (measures of spread), and use these summaries to describe the center, spread, and shape of the data distribution

AU Math: ACMSP248

(Y10) Determine quartiles and interquartile range (ACMSP248)

CCSS Math: 6.SP.5.C

6.SP.5.C Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.

This statistics, data analysis, and probability lesson focuses on computing the median, upper quartile, and lower quartile of a data set. The lesson includes research-based strategies and strategic questions that prepare students for assessments. In this lesson, students read the problem, identifying the data set and what it represents as well as ordering the numbers from least to greatest. Then, they identify the median and calculate the lower quartile and the upper quartile. Finally, they interpret the answer within the context of the problem. In addition to the lesson, there are four pages of Independent Practice and review with questions modeled after current adaptive testing items.

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Standard Alignments:

TEKS Math: 6.12.C

(C) Summarize numeric data with numerical summaries, including the mean and median (measures of center) and the range and interquartile range (IQR) (measures of spread), and use these summaries to describe the center, spread, and shape of the data distribution

AU Math: ACMSP248

(Y10) Determine quartiles and interquartile range (ACMSP248)

CCSS Math: 6.SP.5.C

6.SP.5.C Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.


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