What is a Stem and Leaf Plot?

A stem and leaf plot is a statistical method for organizing and displaying numerical data. It provides a visual representation of the distribution of a dataset, making it easier to identify patterns, clusters, and outliers. In a stem and leaf plot, each data value is split into two parts:

• The stem: Usually the leftmost digit(s) of the number
• The leaf: Usually the rightmost digit of the number

For example, in the number 42, the stem would be 4, and the leaf would be 2. This method allows for a compact display of data while preserving the original values.

How to Use the Stem and Leaf Plot Calculator

Step 1: Enter your data

In the text area provided, enter your numerical data. You can separate values using commas or by placing each value on a new line.

Step 2: Generate the plot

Click the "Generate Stem and Leaf Plot" button. The calculator will process your data and display the results.

Step 3: Interpret the results

The calculator will show two main sections:

a) Stem and Leaf Plot: This table displays the organized data. The left column shows the stems, and the right column shows the corresponding leaves. For example, if you see "2 | 3 4 6 9", it represents the numbers 23, 24, 26, and 29.

b) Basic Statistics: This section provides key statistical measures of your dataset, including:

• Mean: The average of all values
• Median: The middle value when the data is ordered
• Minimum: The smallest value in the dataset
• Maximum: The largest value in the dataset
• Count: The total number of data points

Step 4: Analyze the plot

Look for patterns in the stem and leaf plot. Are there clusters of data? Are there any outliers? The visual representation can help you quickly grasp the distribution of your data.

Step 5: Use the statistics

The basic statistics provided can give you additional insights into your data. For example, comparing the mean and median can indicate if your data is skewed.

By following these steps, you can easily create and interpret a stem and leaf plot for your numerical data, gaining valuable insights into its distribution and characteristics.