General Understanding of Diagrams and the Others
Generally Understanding Diagrams are pictures or graphics that contain information and explain the means, procedures, or activities that are usually carried out by a system. Diagrams can also mean images (sketches, blurred) that use lines and symbols to explain or show something.
Diagram is a picture of data that has been processed in such a way as a graph, line or table. Diagram is a means to facilitate users in analyzing data with an attractive appearance and easy to understand. The contents of the diagram are usually in the form of nominal, scale or statistical data.
The Types of Diagrams are as follows. divided into several types of different shapes and pictures (Taher on Sudan Journals):
Line diagram is a diagram that presents data using lines. Whether it’s a straight line, curve or dotted line. This diagram is usually used to present statistical data obtained through observations from time to time in sequence. In its application, it usually uses the X axis and the Y axis. The X axis is used to indicate the time of observation, while the Y axis is used to show the results of observational values at a certain time. Collection of time and observations form points in the XY plane, then each column of two adjacent points connected by a straight line so that a line chart is created or also called a line graph.
Definition of Line Diagrams. Pie Chart. A pie chart is a diagram that presents data using a circle as a picture. Usually the data presented in the circle diagram is in the form of percent data. In making a pie chart, the first thing you have to do is determine the magnitude of each object’s percentage of the overall data and the size of the center angle of the circle sector.
Understanding Circle Diagrams. Line Grid Diagram. A line box diagram is a diagram whose data is presented using a rectangle accompanied by a line. The statistical data used in describing the grid diagran is in the form of the Five String Statistics, which consists of the smallest data and the largest (extreme) data, Q1, Q2, and Q3.
Understanding Line Charts. Bar Chart. A bar chart is a diagram that uses rectangles as a tool to present data. Generally this diagram is used to illustrate the development of the value of an object of research within a certain period. This diagram presents various kinds of information upright or horizontally and as wide as the separate bars.
Understanding Bar Charts. Presentation in the form of graphic images or diagrams can further explain the problem visually. A bar chart (histogram) is a description of a frequency distribution, where for each class expressed on a horizontal (flat) scale and its frequency on a vertical (upright) scale; or vice versa. Data which are in the form of categories or attributes are very appropriate to be presented with bar charts. If the diagram is made upright, then a flat axis is used to express the attribute. Quantum or data values are drawn on an upright axis.
A histogram is a graphical representation of numerical data distribution. This is an estimate of the probability distribution of continuous variables (quantitative variables) and was first introduced by Karl Pearson. To build a histogram, the first step is to “bin” the range of values — that is, divide the entire range of values into a series of intervals — and then calculate how many values fall into each interval. Garbage is usually defined as successive, non-overlapping variable intervals. Trash (intervals) must be close together, and usually the same size.
If trash is the same size, the rectangle that is erected on top of the bin with a height proportional to the frequency, the number of cases in each bin. In general, however, garbage does not need to be the same width; in that case, an established rectangle has an area proportional to the frequency of cases in the trash  The vertical axis is not frequency but density: the number of cases per unit of variable on the horizontal axis. The histogram can also be normalized to display relative frequencies. It then shows the proportion of cases that fall into each of several categories, with the sum of the heights equaling 1. An example of the variable bin width is shown in the Census Bureau data below.
The histogram gives a rough sense of the distribution density that underlies the data, and often for the density estimation: estimating the probability density function of the underlying variable. The total area of the histogram used for probability density is always normalized to 1. If the length of the intervals on the x-axis are all 1, then the histogram is identical to the relative frequency plot.
A histogram can be thought of as a simple kernel density estimate, which uses the kernel to smooth the frequency of garbage. This results in a smooth probability density function, which in general more accurately reflects the distribution of the underlying variables. The density estimate can be plotted as an alternative to the histogram, and is usually described as a curve rather than a set of squares. Another alternative is the shifted histogram average, which is quick to calculate and provides a smooth curve density estimate without using a kernel.
The histogram is one of the seven basic tools for quality control. Histograms are often confused with bar graphs. A histogram is used for continuous data, where garbage represents the range of data, and meaningful rectangular fields, while bar charts are plots of categorical variables and discontinuities must be indicated by having gaps between rectangles, from which only lengths are meaningful. Often this is ignored, which can cause a confused bar chart for the histogram.
General Understanding of Diagrams and the Others