Graphing Data Sets In Exercises 17–32, organize the data using the indicated type of graph. Describe any patterns.


Life Spans of Houseflies Use a dot plot to display the data, which represent the life spans (in days) of 30 houseflies. 
9 9 4 11 10 5 13 9 7 11 6 8 14 10 6 
10 10 7 14 11 7 8 6 13 10 14 14 8 13 10

Verified step by step guidance

1. Step 1: Understand the problem. You are tasked with creating a dot plot to display the life spans (in days) of 30 houseflies. A dot plot is a simple graphical representation where each data point is represented by a dot above its corresponding value on a number line.
2. Step 2: Organize the data. Start by listing the data values in ascending order: 4, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 10, 10, 10, 11, 11, 11, 13, 13, 13, 14, 14, 14, 14.
3. Step 3: Draw a horizontal number line. Label the number line with the range of values in the data set, which is from 4 to 14. Ensure the scale is evenly spaced and includes all integers within this range.
4. Step 4: Plot the data. For each value in the data set, place a dot above the corresponding number on the number line. If a value appears multiple times, stack the dots vertically above that number to represent the frequency of that value.
5. Step 5: Analyze the dot plot. Look for patterns, such as clusters, gaps, or peaks. For example, you might observe that the most frequent life span is 10 days, and there is a cluster of values between 10 and 14 days. Describe these patterns in your analysis.

Key Concepts

Dot Plot

A dot plot is a simple graphical display used to represent individual data points in a dataset. Each value is represented by a dot above a number line, allowing for easy visualization of frequency and distribution. This type of graph is particularly useful for small datasets, as it clearly shows the occurrence of each value and helps identify patterns or clusters in the data.

Data Distribution

Data distribution refers to the way in which data values are spread or arranged across a range. Understanding the distribution helps in identifying patterns such as central tendency, variability, and the presence of outliers. In the context of the life spans of houseflies, analyzing the distribution can reveal insights about the typical lifespan and any variations among the flies.

Patterns in Data

Patterns in data refer to recognizable trends or regularities that emerge when data is visualized. These can include clusters, gaps, or overall shapes in the graph. Identifying patterns is crucial for drawing conclusions and making predictions based on the data, as it helps to understand the underlying characteristics of the dataset, such as common life spans among houseflies.