DataHour: An Introduction to Central Limit Theorem

DataHour: An Introduction to Central Limit Theorem

27 Dec 202215:12pm - 27 Dec 202216:12pm

DataHour: An Introduction to Central Limit Theorem

About the Event

The Central Limit Theorem is a fundamental statistical principle that states that, under certain conditions, the distribution of the sum or average of a large number of independent and identically distributed random variables will be approximately normal (bell-shaped). This is true even if the individual variables themselves are not normally distributed.

The Central Limit Theorem has several important implications for statistical analysis. For example, it can be used to justify the use of the normal distribution as a model for the sampling distribution of the sample mean. It also provides a basis for the use of statistical techniques that rely on the assumption of normality, such as t-tests and ANOVA.

The Central Limit Theorem is often used in the analysis of data from experiments, surveys, and other observational studies. It can help to provide a statistical foundation for making inferences about a population based on a sample drawn from that population. The conditions under which the Central Limit Theorem holds true include:

  • The random variables must be independent and identically distributed.
  • The sample size must be sufficiently large (typically at least 30).

In this DataHour, Munmun will explain the logic, application and conclusions drawn from the Central Limit Theorem. 

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Who is this DataHour for?

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About the Speaker

Munmun Das

Munmun Das

Principal – Automation and Innovation at Accenture

Munmun is a professional and research scholar with 16 + years of experience in Software Project Management, Product development, Product strategy, and delivery of large-scale, complex automation/transformation solutions, web and mobile applications. She has also mentored industry tech-force as an Industry Speaker, Corporate Mentor, and Guest Lecturer. She has implemented handshaking projects on RPA with Dot Net web applications and successfully built on-demand RPA. 

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