Chemical Science Important Formulas - DATA ANALYSIS| Download the PDF!

By Renuka Miglani|Updated : January 13th, 2022

Hello Aspirants,

While preparing for CSIR NET 2022 exam, you must come across different formulas that are hard to remember and as a result, these formulas might not be remembered by the candidates due to exam fear or pressure, and we start finding them in books or notes from where we have studied.

BYJU'S Exam Prep has the solution for all your doubts and queries! We do not want our students to lag anywhere during the preparation, so we have come up with the concept of Formula Sheets.

In this article, we have provided all the formulas of the topic, Data Analysis. These formula sheets are curated by our experienced subject-matter experts. Aspiring candidates can check and download the PDF for the last minute revision.

Most Important Formulas of Data Analysis (Download PDF)

It is the process by which useful information can be discovered through inspecting data. It is used in different business, social science domains and science. Nowadays, it plays a very important role in making decisions in a more scientific way. In other words, it is the process through which data is organized to draw conclusions. This analysis uses analytical as well as logical reasoning to gain information from data.

Mean and Standard Deviation:

Mean: It Is an Average or In Other Words, Collection of Numbers. It Is Also Known by The Name of Expected Value. It Is the Sum of All Values Divided by The Number of Numbers in the Collection. Its Formula Is:


Standard Deviation: It measures the amount of variation for a given set of values. When a standard deviation is low, it indicates the values are close to mean while high standard deviation indicates the values are spread over a wider range. Its formula is:


Absolute error: It is the difference between actual and measured value. If x is actual value and x0 is the measured, then absolute error will be calculated as:

Δx = x0 - x

Here, Δx is called an absolute error.

Relative error: It is the ratio of absolute error to actual value. If x, x0 and Δx are actual, measured, and absolute quantities respectively, then relative error can be calculated as:

Relative error = 


Covariance and correlation coefficient:

Covariance: It basically measures the relationship between two random variables. It can have both positive and negative value.

  •       Positive covariance indicates that two variables will move in the same direction.
  •       Negative covariance indicates that two variables will move in opposite directions.

Its formula is:



  •       X is a random variable.
  •       E(X)=𝜇 the mean of the random variable X.
  •       E(Y) = v is the mean of the random variable Y.
  •       n = the number of items in the data set.

Correlation Coefficient: It measures the strength of the linear relationship between two variables. The range of values is between -1.0 and 1.0. Its formula is:


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