In order to classify information obtained in the form of data, it is necessary to broadly classify them in series. This type of series has its own characteristics and they obey some general principles. Such types of series are very important for researchers and economists to gain insights so that they can use them for actionable purposes.
Data is important for researchers but in its raw form, it is hardly usable. Therefore, the users of data need to make it presentable and actionable. In order to do so, they need to classify data. This classification can be of many types. However, making a series of data is considered the best type of classification.
Statistical series refers to an arrangement of classified data in a logical manner. Such arrangements follow some unique orders such as size or time of occurrence. The aim of statistical series is to classify data depending on measurable or non-measurable characteristics.
As the name suggests, a statistical series is a special purpose series subject to statistics. In statistical measurement, sometimes the researchers need to rely on classified data. Therefore, when data about populations are collected, they are made available in series so that extraction and measurement become easier. That is why statistical series is so important for scientists and researchers alike.
Statistical series can be prepared depending on the size of the population or the time when the data occurred.
For example, statistical series can be prepared on the bank balances of individuals which will fall under the size variant while the series made on the data of deposits of money will fall under the time of occurrence.
Statistical series can be divided into the following types:
In individual series, the data is presented in a raw form. In other words, there is no frequency in individual series. Only the raw data is classified in terms of a series in the case of individual series.
Put simply, individual series is a presentation of data in raw form. If there is a repetition of data, it is repeated as and when the data is observed. Often, individual series present the numerical value of raw data in a form of series. There is no frequency or class of items in such a series.
As the data in an individual series cannot be classified in terms of frequency, data obtained in many instances are presented as many times as it is obtained. Individual series of data are presented in two forms, ascending and descending orders.
Ascending order: In individual series where ascending order is required, data is delivered in an increasing pattern. In other words, the value of data is presented from the smaller to larger forms in the series.
For example if the marks obtained by five top students in a test are the following:
80, 85, 90, 95, 98
Then the series in ascending pattern will be 80, 85, 90, 95, 98
Descending order: While presenting data in a series in descending form, the series is made with data from larger to smaller values.
For example, the above series of marks in descending order will be: 98,95,90,85,80
Individual series is the simplest and most common form of data representation in statistics. However, problems exist with individual series as data is not classified into actionable forms. The researcher or users of individual series need to make arrangements or improve data representations for quicker reference during the use of data.
In discrete series, data obtained in raw form are presented along with their frequencies. In such a series, data are not presented in ascending or descending manner.
Instead, the data and its frequencies are presented in a tabular or grouped manner.
For example, if the monthly wages of five employees of a company are 10,000, 12,000, 10,000, 12,000, 13,000, 14,000, and 15,000, then the discrete series will be made as follows
| Wages | Frequency |
|---|---|
| 10,000 | 2 |
| 12,000 | 2 |
| 13,000 | 1 |
| 14,000 | 1 |
| 15,000 | 1 |
The continuous series consists of a lot of data where the frequency of data is best represented in a grouped manner. In such a series, there is a form of interval. Unlike discrete series where one datum is present many times or in various frequencies, in the case of continuous series, an interval of five or ten values of the data is taken as a group.
In continuous series, the individual data fall into groups, which can happen many times.
For example, if marks obtained by 100 students in a class are formed with intervals of marks such as 30̅–45, 45–60, 60–75, 75–90, 90–105 and 30 students get marks in the interval of 60–75, then the 60–75 interval will have a frequency of 30.
Similarly, if 15 students get marks within the 75–90 category, the interval of the 75–90 category will be 15.
| Marks obtained | Frequency |
|---|---|
| 30–45 | 26 |
| 46–60 | 24 |
| 61–75 | 30 |
| 76–90 | 15 |
| 91–05 | 5 |
The continuous series is named continuous because where the highest value of one interval ends, the lowest value of the next interval begins. Therefore, data being classified falls in one group or another when the classification is performed. The nature of such classification is that it contains the raw form of data classified in various groups.
Continuous series have a uniqueness in that the intervals or limits of data are continuously monitored in the form of a series. That is why it is called continuous series in statistics. The features of continuous statistical series include the width of intervals, the mid-point of each interval group, upper and lower limits (known as intervals), etc.
Statistical series have other advantages too. Making a series and providing data in a representable form is advantageous for all. The researcher who uses data and those who need insight can get enough help from statistical series. It makes for quick reference ready for insight when data is represented in series form. Therefore, for any useful purpose where the data needs to be used, statistical series can be of great help.
Statistical series are also helpful because they show the correct usability and utility of raw data. In raw form, unusable data may look as if they are useful but the truth of whether they can be used or not is clarified when data is presented in series form. That is why statistical series are considered helpful in performing many statistical functions due to their usefulness and utility in providing data in actionable and accurate form.
Q1. What is the biggest reason for presenting data in the form of a series?
Ans. The biggest reason for converting raw data into series is to get ready and dependable insight from the data. As raw data is not useful enough for scientists as they are not classified, users need to convert them into series to make them actionable.
Q2. What are some forms of study where statistical series are used?
Ans. Statistical series are used in economics, marketing, and all forms of research and science as it offers useful information to make correct inferences.
Q3. Is the frequency of data required in individual series?
Ans. No. The frequency of data is not required in making individual series.
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