Also known as a dot graph, it is used to depict certain data trends in various fields. This graph is plotted by organizing one group of data set on the horizontal axis, and the other on the vertical axis. The corresponding data points for each pair of data is indicated with a set of dots drawn directed towards the x-axis. The dots will combine to form a straight line, and this will be repeated for other variables in the dataset.
Dot plots, just like many other data visualization methods can refer to a variety of graph styles. For the sake of this article, we will be considering 2 prominent types of dot plots, namely; the Cleveland dot plot and the Wilkinson dot plot. These various graph styles are used in data analysis, but the type used for a particular project depends on the goal of the data analyst. As the name implies, one thing all these graph styles have in common is that they all contain dots.
Named after Leland Wilkinson, its distinct feature is using a local displacement that is perpendicular to the scale in order to prevent the dots from overlapping.
At the time of publishing this dot plot algorithm, he was an Adjunct Professor of Statistics from Northwestern University, Evanston. In this paper , he mentioned that the existing programs do not correctly reproduce dot plots. Instead, they used regular class intervals to produce plots that are similar to the line printer asterisk histograms from way back. It is sometimes said to be similar to a histogram because of its vertical one-dimensional display.
The difference, however, is that unlike histogram which uses length to encode data values, Cleaveland dot plot uses position. He further broke down graph estimation into 3 parts, namely; discrimination, ranking, and rationing. Cleveland and Wilkinson also worked together on a book titled, The Grammar of Graphics. This book influenced the creation of Graph Builder.
Example 1: The table below describes the average number and types of pizzas ordered from a pizza store in a week. Draw a dot plot for the table. Solution 1: In the digital illustration of a dot graph below, we chose our x-axis data to be Bacon, Cheese, and Pepperoni. The y-axis , on the other hand, was represented by the number of orders received daily. Example 2: Consider the graph below which represents the pizza orders received from Monday to Wednesday by a small scale restaurant and their prices.
Use the graph to determine the mean number of orders received each day. Solution: The mean number of orders received daily can be calculated by counting the number of dots for each day, adding them together, and dividing by 3. A mode is the highest occurring number. From the dot graph, we observe that 5 orders were received on Monday, 3 orders on Tuesday, and 3 orders on Wednesday.
Since, 3 orders were received on Tuesday and Wednesday, then the most occurring number of orders is 3. Try to identify the cause of any outliers. Correct any data-entry errors or measurement errors.
Consider removing data values that are associated with abnormal, one-time events special causes. Then, repeat the analysis. Multi-modal data have multiple peaks, also called modes. Multi-modal data often indicate that important variables are not yet accounted for. If you have additional information that allows you to classify the observations into groups, you can create a group variable with this information.
Then, you can create the graph with groups to determine whether the group variable accounts for the peaks in the data. Interpret the key results for Dotplot Learn more about Minitab.
Complete the following steps to interpret a dotplot. Step 1: Assess the key characteristics Examine the peaks and spread of the distribution. Peaks and spread Identify the peaks, which are the bins that have the most dots. Sample size N The sample size can affect the appearance of the graph. Step 2: Look for indicators of nonnormal or unusual data Skewed data and multi-modal data indicate that data may be nonnormal. Skewed data When data are skewed, the majority of the data are located on the high or low side of the graph.
Outliers Outliers, which are data values that are far away from other data values, can strongly affect your results. Multi-modal data Multi-modal data have multiple peaks, also called modes. For example, a manager at a bank collects wait time data and creates a simple dotplot.
The dotplot appears to have two peaks. Upon further investigation, the manager determines that the wait times for customers who are cashing checks is shorter than the wait time for customers who are applying for home equity loans. The manager adds a group variable for customer task, and then creates a dotplot with groups. This is one way to think about the center of the distribution. We also want to describe how much the data varies among individuals in the group. Variability is another word for spread.
We describe the spread in two ways:. The sugar content in adult cereals is skewed to the right. Many adult cereals have less than 8 grams of sugar in a serving. A smaller number of adult cereals contain high amounts of sugar. Comment: There is nothing special about the number 8.
We chose 8 as a convenient reference point to describe the opposite trends in these two distributions. A typical adult cereal has 3 grams of sugar in a serving.
Comment: Here we looked at the most common value in each distribution. We develop more precise ways to describe the center of a distribution in the next section. For now, just choose a reasonable typical value to represent the group. Overall range: Adult cereals have 0 to 14 grams of sugar in a serving. So both types of cereal vary over a range of 14 grams.
Comment: Here we looked at clumps in the data to identify a range of typical values. We develop more precise ways to describe the spread a distribution in the last two sections of this module. It is not uncommon for adult cereals to have 0 to 6 grams of sugar in a serving. Larger amounts of sugar are less common.
0コメント