Fundamental Concepts of Statistics that are useful for CAIIB aspirants

In our CAIIB compulsory paper “Advanced Bank Management”, business mathematics module is an important module for scoring the pass marks.  Therefore it is necessary to read Business Mathematics module thoroughly.  In our syllabus almost half of the units of Business Mathematics module has Statistics concepts. So learning the fundamental concepts of Statistics are very important to CAIIB aspirants for preparation of Advanced Bank Management paper. 

Introduction to Statistics:

Statistics is one of the branches of mathematics  dealing with large volume of data for collection, analysis, presentation and interpretation of data into a mathematical form for arriving meaningful information from those data.  Statistics Methods are applied in different fields of science, for analysing business and it is also used in financial markets.

Statistics has set of concepts, rules and procedures that helps us to organise and present the large volume of data in the form of tables, graphs, charts etc,. Statistical techniques helps us to analyse the organised data for our understanding. In turn it help us to make meaningful or useful decisions.

What is data?

Data or Data set means list of facts, observations or figures from which we can draw a conclusion. Example: Marks scored in different subjects by each student of a class. Here marks are data which helps us to get conclusion like pass percentage of class.

  1. Quantitative Data: Data which are result of some measurements. Example: marks, temperature, weight, etc,.
  2. Qualitative Data: Data that are grouped together based on some property or quality. Example: gender, subjects,  types of vehicles etc,.

What is Variable?

Variables are nothing but a sub set of data pertaining to property or attributes of a fact, observation or figure which take different values. Example : Marks scored in different subjects by each student of a class. Here mark is a variable because mark is the attribute (property) of the subject which changes to each student.

  • Discrete Variable – They are limited in number of values. Example “number of cars parked in a parking area”, here the number of car is limited by the size of the parking area.
  • Continuous Variable – Opposite of discrete variable means they have infinite number of values. Example time.
  • Independent Variable – It is the variable which changed or controlled in a scientific experiment to test the effects on another variable is called Independent variable. For example radius of the circle affects area of the circle, therefore radius is a Independent variable
  • Dependent Variable – A variable that depends on independent variable. For example area of the circle is depended upon the radius, therefore area of the circle is dependent variable.
  • Quantitative Variable – Values which are measurable such as marks, temperature etc,.
  • Qualitative Variable – Values that are grouped together based on some property or quality. Example: gender, subjects,  types of vehicles etc,.

What is Population and What is Sample?

In statistics, the term Population refers to all set of items from data that are to be studied. While sample is a part of population chosen to analyse.  For example, let’s say a shirt manufacturer produced  1000 shirts and he wants to check the quality of the shirts before dispatching them to retail stores. It is very costly and time consuming  to check all the 1000 shirts (this is population). Instead the manufacturer checks the quality of 20 shirts (this is sample) by examining the one shirt of every 50 shirts. Based on that he arrives conclusion that all shirts are likely to be stitched correctly.

Distribution of data:

The distribution of data set is nothing but organising  and summarising the values to examine the features of the data. In simple terms plotting all values of the data in a graph for meaningful observation about the features of the data set.

Different Methods of Statistics:

The above terms are fundamental concepts of statistics. Therefore it is necessary to understand them as they are commonly used in the statistical methods.

After organising the data we have to analyse the data. There are two main statistical methods used in analysing the data.

  1. Descriptive Statistics: Using the data from a sample to describe the properties of a population through numerical calculations and by using tables, graphs and charts.
  1. Inferential Statistics: Process of predicting (inferring) the properties of a population by analysing the sample data.

Descriptive Statistics:

Descriptive Statistics helps us to understand the features of the data set, using the details about the samples or population. They use tools such as numerical calculations, tables, graphs, charts, shapes etc,. for describing the properties of the population or sample.

Descriptive Statistics mainly uses graphs; The data sets are plotted in the graph to have pictorial representation of the data . By using them we measure the following three properties of the data in Descriptive Statistics.

  1. Measures of Centre: Several statistics which are used to measure the central tendency or how the data are bunched when the data are plotted in the graph is called measures of centre.  The following are the three statistical parameters which are used to describe the central distribution of the data set.
    • Mean
    • Median
    • Mode
  1. Measures of Spread: Measures how much the data are spread out from the center of the distribution.  The following are the key measures of spread or variability.
  • Range
  • Interquartile Range (IQR)
  • Variance
  • Standard Deviation
  1. Measures of Shape: Distributions that are from continuous data will form different shapes of rises and drops. The following are different types of shapes arises from the distribution
  • Symmentric
  • Sknewness
  • Kurtosis

Inferential Statistics or Statistical Inference:

Inferential Statistics helps us to predict the characteristics from the data. Unlike descriptive statistics, here the features of the large population is inferred or predicted by using the sample. They help us to know about the strength of the relationship between the independent variables and dependent variables.

The following are the some statistics methods used in the inferential statistics or statistical inference.

  • Correlation and Regression
  • Estimates
  • Statistical Tests or Hypothesis Testing.


With this we have touched the surface of the fundamental concepts of statistics. Studying these fundamentals before reading the business mathematics module of Advanced Bank Management book will help to read and understand the topics such as Sampling Methods, Standard Deviation, Correlation, Regression, Probability distribution etc,.

For better preparation of ABM paper, buy the  Advanced Bank Management Book now. 



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