Amrita Vishwa Vidyapeetham’s Integrated M. Tech.+ Ph. D. is an offering that has been an outcome of its pioneering work in impactful research towards society over the past decade. By crystallizing challenges in emerging areas that are of high relevance from techno-social and Industry perspective, this integrated program ensures students are part of a structured program and graduate with mentorship from some of the most recognized faculty in Amrita and its International partners.

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Amrita Vishwa Vidyapeetham interview questions for popular designations

### 1 What is DOE?

DOE is an acronym for the Design of Experiments in statistics. It is considered as the design of a task that describes the information and the change of the same based on the changes to the independent input variables.

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### 4 What is the difference between the Ist quartile, the IInd quartile, and the IIIrd quartile?

Quartiles are used to describe the distribution of data by splitting data into three equal portions, and the boundary or edge of these portions are called quartiles.

**The lower quartile (Q1)**is the 25th percentile.

**The middle quartile (Q2)**, also called the median, is the 50th percentile.

**The upper quartile (Q3)**is the 75th percentile.

### 4 Given a left-skewed distribution that has a median of 60, what conclusions can we draw about the mean and the mode of the data?

Given that it is a left-skewed distribution, the mean will be less than the median, i.e., less than 60, and the mode will be greater than 60.

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### What are the types of biases that you can encounter while sampling?

There are three types of biases:

### 3 In an observation, there is a high correlation between the time a person sleeps and the amount of productive work he does. What can be inferred from this?

First, correlation does not imply causation here. Correlation is only used to measure the relationship, which is linear between rest and productive work. If both vary rapidly, then it means that there is a high amount of correlation between them.

### 2 What are population and sample in Inferential Statistics, and how are they different?

A population is a large volume of observations (data). The sample is a small portion of that population. Because of the large volume of data in the population, it raises the computational cost. The availability of all data points in the population is also an issue.

In short: