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We have already discussed techniques for visually representing data (see histograms and frequency polygons). In this section, we present another important graph called a box plot. Box plots are useful for identifying outliers and for comparing distributions. We will explain box plots with the help of data from an in-class experiment. As part of the "Stroop Interference Case Study," students in introductory statistics were presented with a page containing 30 colored rectangles. Their task was to name the colors as quickly as possible. Their times (in seconds) were recorded. We'll compare the scores for the 16 men and 31 women who participated in the experiment by making separate box plots for each gender. Such a display is said to involve parallel box plots.

There are several steps in constructing a box plot. The first relies on the 25th, 50th, and 75th percentiles in the distribution of scores. Figure 1 shows how these three statistics are used. For each gender, we draw a box extending from the 25th percentile to the 75th percentile. The 50th percentile is drawn inside the box. Therefore,

the bottom of each box is the 25th percentile,

the top is the 75th percentile,

and the line in the middle is the 50th percentile.

The data for the women in our sample are shown in Table 1.

step-by-step explanation:

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can you take a photo of the screen?