ANOVA is popularly known as analysis
of variance. It was developed in the 1920s by a famous statistician called Robert
Fisher. ANOVA is a collection of statistical tools that are used to draw
significant conclusions about various data sets. Since 1920 this area has
advanced in a higher dimension with new changes. Improvements have facilitated
statisticians to infer meaningful findings across multiple research areas. For that reason, ANOVA has been incorporated
as a universal module to students pursuing statistics as a major. It's an
exciting area of study which demands a full understanding of what you are
doing. You don't jump into questions, but you must have serious comprehension
of data set facets. Which are vital basics you must know before tackling any
problem related to ANOVA?
1. The
variance- It will help you to draw necessary inferences by comparing results
from diverse sets of data.
2. Standard
deviation- Understand how to quantify each data in a set of different mean
observations.
3. The T-
tests- You must be able to analyze the two means given through statistical examinations
to get correct answers.
4. Hypothesis
testing - Ensure you can determine results from the different samples. By
testing hypothesis, you should either reject or accept the inferences you
obtain reflecting the population.
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