Chapter 3: Displaying and Summarizing Quantitative Data

Vocabulary flashcards for key terms related to the display and summarization of quantitative data.

Histogram

A visual representation of quantitative data, slicing the data into equal-width bins to show the distribution.

Relative Frequency Histogram

A histogram displaying the percentage of cases in each bin.

Stem-and-Leaf Display

A quantitative data display, similar to a histogram, that preserves individual data values.

Dotplot

A simple display placing a dot along an axis for each data point.

Quantitative Data Condition

The data must be values of a quantitative variable with known units.

Modes

The peaks or humps in a histogram, representing frequently occurring values.

Unimodal

A histogram with one main peak.

Bimodal

A histogram with two peaks.

Multimodal

A histogram with three or more peaks.

Uniform Distribution

A histogram where all bars are approximately the same height, indicating no apparent mode.

Tails

The thinner ends of a distribution, representing extreme values.

Skewed

Describes a histogram where one tail stretches out farther than the other, indicating asymmetry.

Outliers

Values that stand off away from the body of the distribution; unusually large or small values.

Median

The value with exactly half the data values below it and half above it; the middle value.

Range

The difference between the maximum and minimum values in a data set; a simple measure of spread.

Interquartile Range (IQR)

A measure of spread focusing on the middle of the data, ignoring extreme values; calculated as Q3 - Q1.

Quartiles

Values that divide the data into four equal sections: Q1 (25th percentile), Q2 (median), and Q3 (75th percentile).

5-Number Summary

Reports the minimum, Q1, median, Q3, and maximum of a distribution; provides a comprehensive overview of the data's range and center.

Boxplot

A graphical display of the five-number summary, useful for comparing distributions and identifying outliers.

Mean

The point where the histogram balances; calculated by averaging the data values; sensitive to outliers.

Deviation

A measure of how far each data value is from the mean; can be positive or negative.

Variance

The average of the squared deviations from the mean; measured in squared units. Formula: s^2 = RAC{\SUM(x-\bar{x})^2}{n-1}

Standard Deviation

The square root of the variance; measured in the same units as the original data. Formula: s = \sqrt{\fRAC{\SUM(x-\bar{x})^2}{n-1}}