Which statement best summarizes the information on these graphs?

Which statement best summarizes the information on these graphs?

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A two-part text alternative is needed in these cases. The short description is used to describe the picture and, where applicable, to show where the long description can be found. The long explanation is the second part, which is a textual representation of the image’s vital detail. The following examples demonstrate various approaches to providing such short and long descriptions.
There are times where the picture composition is critical and must be included in the long definition. In addition to the actual values and patterns that the chart represents, the sequence of colors used and the relative heights of the columns in a bar chart can be important information about the chart’s structure.
Keep in mind that abstract images can be difficult to comprehend for many people, particularly those with learning disabilities or poor vision. Long explanations help a lot of people, so making them accessible to all, for example as part of the main content, is a good idea. It may also be possible to simplify the pictures and make them more understandable to all.

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A common graphing tool is the histogram. It’s used to summarize data that’s either discrete or continuous and calculated on an interval scale. It is often used to visualize the major features of the data distribution in a convenient format. In a data set, a histogram divides the spectrum of possible values into classes or categories. A rectangle is drawn for each group, with a base length equal to the range of values in that group and an area proportional to the number of observations in that group. This means that the rectangles would have different heights. A histogram resembles a vertical bar graph, but there are no gaps between the bars while the variables are continuous. When the variables are discrete, however, gaps between the bars should be left. A clear example of a histogram is shown in Figure 1.

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Shopping or going to work are two options. How long do you think it’ll take you to get there? “Well, it normally takes about 45 minutes to get to work, but it can take up to hours if traffic is heavy,” you may have said. In other words, a typical time is 45 minutes, while mode. 11.1 % Calculating a Data Set’s Mean You’ve probably heard of the mean; it’s the most popular form of average and takes all of the data into account. Let’s take a look at an example to better understand this form of average. Assume eight students took an exam and earned the following grades: 976-784-43 Examine the exam scores to determine what a normal value would be to get a sense of the problem and to ensure that your final response is fair. Make a mental note of how much you think it would cost. To find the mean value, add all of the data values together and divide by the number of data points. The number of the data values in this example is place). What was the difference between this and the standard value you calculated at the start? Did you determine that a standard value would be 6 or 7? The mean value will always be between the smallest and largest values, and will frequently be in the center, though the exact location will depend on the data set’s actual values. Getting to the Mean The following is a summary of how to calculate a mean: As an alternative,

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You can gather and analyze data from field investigations, surveillance devices, vital statistics, and other sources as a field epidemiologist. This mission, known as descriptive epidemiology, seeks to address the following questions about the incidence of illness, injury, or environmental hazards:
A definition of the illness or health condition is given in response to the first query. The question “How much?” is answered in terms of counts or fees. The last three questions are evaluated as time, location, and individual patterns of the data. After the data has been organized and presented, descriptive epidemiology entails analyzing the trends, which is often done by comparing them to predicted patterns or norms (e.g., historical counts, enhanced monitoring, or performance from prevention and control programs). Descriptive epidemiology serves a variety of purposes through the organization, inspection, and evaluation of data (Box 6.1).
Putting Epidemiologic Data in Order
Organizing descriptive data into tables, graphs, diagrams, maps, or charts allows you to understand the information quickly, objectively, and coherently. If the tables or graphs are used to help the investigator interpret the data or to illustrate it in a study or to an audience, their company should be able to easily show the main trends and exceptions to those patterns. A title, details, footnotes, and text are all common elements in tables, graphs, maps, and charts (Box 6.2). Where the particular data display form is first applied in this chapter, additional guidelines for preparing these data displays will appear.