This page would contain resource information like a glossary of terms from the section, key equations, and a reminder of concepts that were covered.
Make this more relevant to what students want — help them to build their processes, study guides, mnemonics, and memory dump material.
Essential Concepts
- A boxplot captures only the median of the dataset, not the mean, as a measure of center. It provides a quick glance (or summary) of the data to make comparisons based on the median, skew, outliers, and percentiles.
- The collection of the minimum, first quartile, median, third quartile, and maximum form the five-number summary of the variable.
- There are several good methods to use for determining an observation to be an outlier in the distribution. The IQR method commonly uses a distance 1.5 times IQR from Q1 or Q3.
Key Equations
- Interquartile range (IQR)
Q3–Q1
- Upper outlier
Q3 + 1.5 × (IQR), remember to multiply 1.5 by IQR first, then add to Q3
- Lower outlier
Q1 – 1.5 × (IQR), remember to multiply 1.5 by IQR first, then subtract from Q1
Glossary
- first quartile
- the value below which one quarter of the data lies, also equal to the 25th percentile. Sometimes denoted Q1.
- third quartile
- the value below which three quarters of the data lay, also equal to the 75th percentile. Sometimes denoted as Q3.
- interquartile range
- the quantity Q3–Q1. Sometimes denoted IQR.
- five-number summary
- the collection of the minimum, first quartile, median, third quartile, and maximum of the variable.
- upper outlier
- an observation that is greater than Q3 + 1.5 × (IQR).
- lower outlier
- an observation that is less than Q1 – 1.5 × (IQR).
Put formal DCMP I Can statements to prepare for the self-check.
These I Can Statements are new (the first two are the “you will understand” rephrased as an I Can):
- I can provide visual summaries of quantitative variables using boxplots.
- I can compare the distributions of multiple populations using boxplots.
- I can compare and draw inferences from boxplots.
