Introduction to Statistics

What is Statistics?

Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistics is a highly interdisciplinary field; research in statistics finds applicability in virtually all scientific fields and research questions in the various scientific fields motivate the development of new statistical methods and theory. In developing methods and studying the theory that underlies the methods statisticians draw on a variety of mathematical and computational tools.

Types of Graphs 

In statistics, we generally come across with vast sets of data. Just to recall that statistics is a science of numbers that is concerned with collection, organization, measurement, and analysis of the numerical data. It usually deals with the quantity more than quality. Sometimes, it becomes really hectic to work with a large amount of data. There are tedious and lengthy calculations in

statistics. It is easier to draw graphs for such problems and calculate the results from it. The graph makes large data quite simpler to work with. The graph is pictorial representation of the numerical data. Graphs are a very effective visual tool because they have the capacity to present the information quickly as well as easily. That is the main reason why the graphs are commonly used by different media and also in business.


There are different kinds of graphical charts based on statistics as follows:

               1. Line graphs
               2. Pie charts
               3. Bar graph
               4. Scatter plot
               5. Stem and plot
               6. Histogram
               7. Frequency polygon
               8. Frequency curve
               9. Cumulative frequency or ogives

A line graph is a diagram that shows a line joining several points, or a line that shows the relationship between the points. A line graph can be taken as xy plane, where there will be an independent variable and a dependent variable and it specifies how the two variables are related to each other and vary with respect to one another. Mostly, the independent variable is taken on the x-axis while the dependent variable on the y-axis.

A pie chart can be taken as a circular graph which is divided into different disjoint pieces, each displaying the size of some related information. The highlight of this graph is that it represents a whole and each part represents a percentage of the whole. Hence, pie charts are best used with respect to categorical data which helps one understand what percentage each of these category constitutes. It also has a good visual treat and the percentage value of each section is instantly known.

Bar graph is drawn on an x-y graph and it has labelled horizontal or vertical bars that show different values. The size, length and color of the bars represent different values. Bar graph is very useful for non continuous data and it helps in comparing or contrasting the size of the different categories of the data provided.

A scatter plot or scatter graph is a type of graph which is drawn in Cartesian coordinate to visually represent the values for two variables for a set of data. It is a graphical representation that shows how one variable is affected by the other. The data is presented in the form of collection of points, each of which has one value of a variable positioned on the horizontal or x-axis, also called explanatory variable and the value of the other variable positioned on the vertical or y-axis, also called response variable.

Histogram is the most accurate graph that represents a frequency distribution. In the histogram the scores are spread uniformly over the entire class interval. The class intervals are plotted on the x-axis and the frequencies on the y-axis. Each interval is represented by a separate rectangle.
The area of each rectangle is proportional to the number of measures within the class- interval. The entire histogram is proportional to the statistical data set.

The frequency polygon has most of the properties of a histogram, with an extra feature. Here the mid point of each class of the x-axis is marked. Then the midpoints and the frequencies are taken as the plotting point. These points are connected using line segments. We also complete the graph, that is, it's closed by joining to the x-axis. Frequency polygon gives a less accurate representation of the distribution, than a histogram, as it represents the frequency of each class by a single point not by the whole class interval.