Finally, tables are useful for summarizing and comparing quantitative information of different variables. However, the interpretation of information takes longer in tables than in graphs, and tables are not appropriate for studying data trends.
Furthermore, since all data are of equal importance in a table, it is not easy to identify and selectively choose the information required. For a general guideline for creating tables, refer to the journal submission requirements 1.
Heat maps help to further visualize the information presented in a table by applying colors to the background of cells. By adjusting the colors or color saturation, information is conveyed in a more visible manner, and readers can quickly identify the information of interest Table 2.
All numbers were created by the author. Whereas tables can be used for presenting all the information, graphs simplify complex information by using images and emphasizing data patterns or trends, and are useful for summarizing, explaining, or exploring quantitative data. While graphs are effective for presenting large amounts of data, they can be used in place of tables to present small sets of data.
A graph format that best presents information must be chosen so that readers and reviewers can easily understand the information. In the following, we describe frequently used graph formats and the types of data that are appropriately presented with each format with examples. Scatter plots present data on the x - and y -axes and are used to investigate an association between two variables.
A point represents each individual or object, and an association between two variables can be studied by analyzing patterns across multiple points. A regression line is added to a graph to determine whether the association between two variables can be explained or not. If multiple points exist at an identical location as in this example Fig.
In this case, a correlation coefficient or regression line can be added to further elucidate the correlation. A bar graph is used to indicate and compare values in a discrete category or group, and the frequency or other measurement parameters i. Depending on the number of categories, and the size or complexity of each category, bars may be created vertically or horizontally.
The height or length of a bar represents the amount of information in a category. Bar graphs are flexible, and can be used in a grouped or subdivided bar format in cases of two or more data sets in each category. The mean and standard deviation of the VAS scores are expressed as whiskers on the bars Fig.
By comparing the endpoints of bars, one can identify the largest and the smallest categories, and understand gradual differences between each category. It is advised to start the x - and y -axes from 0. Illustration of comparison results in the x - and y -axes that do not start from 0 can deceive readers' eyes and lead to overrepresentation of the results. One form of vertical bar graph is the stacked vertical bar graph. A stack vertical bar graph is used to compare the sum of each category, and analyze parts of a category.
While stacked vertical bar graphs are excellent from the aspect of visualization, they do not have a reference line, making comparison of parts of various categories challenging Fig.
A pie chart, which is used to represent nominal data in other words, data classified in different categories , visually represents a distribution of categories.
It is generally the most appropriate format for representing information grouped into a small number of categories. It is also used for data that have no other way of being represented aside from a table i.
A pie chart is also commonly used to illustrate the number of votes each candidate won in an election. A line plot is useful for representing time-series data such as monthly precipitation and yearly unemployment rates; in other words, it is used to study variables that are observed over time. Line graphs are especially useful for studying patterns and trends across data that include climatic influence, large changes or turning points, and are also appropriate for representing not only time-series data, but also data measured over the progression of a continuous variable such as distance.
As can be seen in Fig. If data are collected at a regular interval, values in between the measurements can be estimated. In a line graph, the x-axis represents the continuous variable, while the y-axis represents the scale and measurement values.
It is also useful to represent multiple data sets on a single line graph to compare and analyze patterns across different data sets. A box and whisker chart does not make any assumptions about the underlying statistical distribution, and represents variations in samples of a population; therefore, it is appropriate for representing nonparametric data.
AA box and whisker chart consists of boxes that represent interquartile range one to three , the median and the mean of the data, and whiskers presented as lines outside of the boxes. Whiskers can be used to present the largest and smallest values in a set of data or only a part of the data i. Data that are excluded from the data set are presented as individual points and are called outliers. The spacing at both ends of the box indicates dispersion in the data.
The relative location of the median demonstrated within the box indicates skewness Fig. The box and whisker chart provided as an example represents calculated volumes of an anesthetic, desflurane, consumed over the course of the observation period Fig.
Most of the recently introduced statistical packages and graphics software have the three-dimensional 3D effect feature. The 3D effects can add depth and perspective to a graph. However, since they may make reading and interpreting data more difficult, they must only be used after careful consideration.
The application of 3D effects on a pie chart makes distinguishing the size of each slice difficult. Even if slices are of similar sizes, slices farther from the front of the pie chart may appear smaller than the slices closer to the front Fig.
Finally, we explain how to create a graph by using a line graph as an example Fig. In Fig. In many graphs, the x- and y-axes meet at the zero point Fig. The data can be clearly exposed by separating the zero point Fig. Separating the data sets and presenting standard deviations in a single direction prevents overlapping and, therefore, reduces the visual inconvenience.
Doing so also reduces the excessive number of ticks on the y-axis, increasing the legibility of the graph Fig. In the last graph, different shapes were used for the lines connecting different time points to further allow the data to be distinguished, and the y-axis was shortened to get rid of the unnecessary empty space present in the previous graphs Fig. A graph can be made easier to interpret by assigning each group to a different color, changing the shape of a point, or including graphs of different formats [ 10 ].
The use of random settings for the scale in a graph may lead to inappropriate presentation or presentation of data that can deceive readers' eyes Fig. Owing to the lack of space, we could not discuss all types of graphs, but have focused on describing graphs that are frequently used in scholarly articles. We have summarized the commonly used types of graphs according to the method of data analysis in Table 3. Frequency Polygon.
Frequency Curve. Pie Chart Circle Diagram. Example : A study on the number of accidents in the year in a particular area is given below. Draw a line graph to represent the data. Simple bar diagram. Multiple bar diagram. Subdivided bar diagram. Percentage bar diagram. Example : Draw a simple bar diagram using the following data.
Example : Draw a bar diagram using the following data showing the pass percentage of different subjects in five years. Explore Ebooks. Bestsellers Editors' Picks All Ebooks. Explore Audiobooks. Bestsellers Editors' Picks All audiobooks. Explore Magazines. Editors' Picks All magazines. Explore Podcasts All podcasts. Difficulty Beginner Intermediate Advanced. Explore Documents. Uploaded by Sonali Singh. Did you find this document useful? Is this content inappropriate?
Report this Document. Flag for inappropriate content. Download now. For Later. Related titles. The Sandeep Garg Class 11 Economics Solutions Chapter 3 is an easy and scoring chapter that presents how to show data efficiently through the use of diagrammatic presentations like bar charts, histograms, pie charts, and so on.
The Sandeep Garg Class 11 Economics Solutions Chapter 3 is the study of data in a simple and presentable manner through the representation of diagrams.
There are radii at each corner of the circle to divide the area into sectors, so a pie chart consists of a circle. Further, the sector values are proportional to those of the items that are under investigation. Additionally, the circle comprises the entire data set under consideration. Provide a percentage breakdown of the components of the data given. In the center of the circle, the angle is degrees, so divide each percentage component by 3.
Make a circle. Divide the circle into sectors according to the central angles of each percent component. Use different shades for each sector. Line charts or polygons showing frequency distributions are called frequency diagrams. Below is a frequency chart showing the corresponding table results. The midpoint of each group represents the frequency polygon of grouped data.
Histograms are bar graphs that show frequency distributions. Follow the below mentioned important steps on how to create a histogram:.
0コメント