Classification of Data
Classification is the technique of categorizing data into groups that share common characteristics or features. It involves sorting data into homogeneous classes or groups based on their similarities.
Data in its raw form is often difficult to comprehend and unsuitable for analysis and interpretation. The organization of data into categories aids in its comparison and analysis. For instance, the population of a town can be categorized based on factors like gender, age, marital status, and so on.
Objectives of Classification
- Simplification and Briefness
- Utility
- Distinctiveness
- Comparability
- Scientific arrangement
- Attractive and effective
Characteristic of a Good Classification
- Comprehensiveness
- Clarity
- Homogeneity
- Suitability
- Stability
- Elastic
Question for Chapter Notes - Organisation of Data
Try yourself:What is the process of arranging data into homogeneous groups based on their common features?
Explanation
The process of arranging data into homogeneous groups based on their common features is known as classification. Regression is a statistical method used to analyze the relationship between variables. Clustering is the process of grouping similar objects together based on their characteristics. Therefore, option B is the correct answer.
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Raw Data
Raw data is unstructured and requires processing and organisation before it can be used effectively to derive meaningful insights.
Chronological Classification
Chronological classification is a method of categorising data based on time, where the data is arranged either in ascending or descending order with respect to a particular time frame such as years, quarters, months, or weeks. This type of classification is also referred to as "temporal classification".
Question for Chapter Notes - Organisation of Data
Try yourself:What is the purpose of a chronological classification?
Explanation
The purpose of a chronological classification is to group data according to time. This method is useful when analyzing data that vary over time, such as sales figures, population growth, or weather patterns.
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Geographical Classification
Geographical Classification refers to the classification of data based on geographical locations such as countries, states, cities, districts, etc. It is also known as Spatial Classification.
Qualitative Classification
Qualitative Classification is a type of data classification that provides descriptive information about the quality of something or someone. For example, skin color, eye color, hair texture, etc. can give us qualitative information about a person.
Condition Series
Condition Series involves the classification of data according to changes occurring in variables under specific conditions. Variables such as height, weight, age, marks, income, etc. can be used for condition series.
Attributes
Attributes refer to additional information about the characteristics of each spatial data in the survey. For instance, in a population survey, attributes of the individuals may include their name, age, height, weight, etc.
Variable
Variable refers to a characteristic that varies or changes from one investigation to another, such as person to person, time to time, or place to place. It can be a quantity or attribute that has different values.
Question for Chapter Notes - Organisation of Data
Try yourself:What does the term "variable" mean in statistical analysis?
Explanation
In statistical analysis, a variable refers to a quantity or attribute whose value varies from one investigation to another. It is derived from the word ‘Vary’, which means to differ or change. A variable may refer to a characteristic that varies from person to person, time to time, place to place, or any other unit of analysis.
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Class Limits
Continuous variables refer to variables that can take any possible value (including fractions) within a specified range. Examples of continuous variables include temperature, height, weight, and marks.
Class Interval or Class Width
Discrete variables, on the other hand, are variables that can only take exact values and not fractional values. For example, the number of workers or students in a class is a discrete variable, as is the number of children in a family.
Class Mid Point or Class Mark
A frequency distribution table is a comprehensive way of representing the organization of raw data of a quantitative variable. This table shows how various values of a variable are distributed and their corresponding frequencies. Frequency distributions can be classified as discrete or continuous.
- Class limits are the numerical figures that specify the lower and upper limits of a class interval.
- A class interval (or class width) is an interval used to group variable values. For example, a group of people can be classified according to age group, with class intervals such as 15-19 years, 20-24 years, 25-29 years, and so on.
Class Mid Point or Class Mark
The class midpoint (or class mark) is the number in the middle of a class interval, found by adding the upper and lower limits and dividing them by two.
Frequency Curve
A frequency curve is obtained by joining the points of a frequency polygon through a freehand smoothed curve rather than straight lines.
Tally Marking
Tally marking is a method of keeping count using a unary numeral system. The general way of writing tally marks is as a group or set of five lines, where the first four lines are drawn vertically and the fifth line runs diagonally over the previous four vertical lines. Tally marks were used in earlier times when keeping track of individual belongings, such as domestic animals like goats and cows, was challenging.
Question for Chapter Notes - Organisation of Data
Try yourself:What is the purpose of tally marks?
Explanation
The purpose of tally marks is to keep track of numerical data, specifically for counting. In earlier times, tally marks were used to keep track of domestic animals such as goats and cows. The symbol ‘|’ denotes the value 1, and the general way of writing tally marks is as a group or set of five lines. The first four lines are drawn vertically, and each fifth line runs diagonally over the previous four vertical lines, i.e. from the top of the first line to the bottom of the fourth line. Therefore, option A is the correct answer.
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Frequency Array
A frequency array is a way of organizing data for a discrete variable, showing the different values of the variable along with their respective frequencies.
Bivariate Frequency Distribution
When the frequency distribution involves two variables, it is called a bivariate frequency distribution. This type of distribution shows the frequencies of two variables together, such as income and expenditure data of households.
Question for Chapter Notes - Organisation of Data
Try yourself:What is a bivariate frequency distribution?
Explanation
A bivariate frequency distribution is the frequency distribution of two variables. It shows the series of statistical data having frequencies of two variables. For example, the data on income and expenditure of the households can be represented using a bivariate frequency distribution. Therefore, option B is the correct answer.
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Univariate Frequency Distribution
A statistical data series that shows the frequency of only one variable is referred to as univariate frequency distribution. This type of frequency distribution displays the frequency distribution of a single variable, such as the income of people or marks scored by students.
Multivariate Distribution
Multivariate distributions represent the correlation between at least two estimates and the relationships among them. For each univariate distribution with one random variable, there is a broader multivariate distribution. For example, the univariate normal distribution has a broader counterpart, the multivariate normal distribution, which is the most commonly used model for examining multivariate data. However, there are other types of multivariate distributions as well, including the multivariate lognormal distribution, the multivariate binomial distribution, etc.