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Learn ANOVA: A Form of Hypothesis Testing Formula

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Edureify, the best AI Learning App has mentioned the concept of Hypothesis Testing in Machine Learning. There are various forms of Hypotheses Testing in Machine Learning and one of them is ANOVA.

In this article, Edureify will provide information on ANOVA in detail. Read on to know more about ANOVA and learn about it from Edureify’s coding courses.

What is ANOVA?

Analysis of Variance, ANOVA is a statistical formula developed by Ronald Fisher that is used for variance comparison across the means of different groups. ANOVA helps in determining the significance of a survey or experiment. It is a phenomenon that lets one decipher whether one needs to reject the null hypothesis or accept the alternate hypothesis.

ANOVA helps test different groups and understand the difference between them. The following are certain situations when one might require to test different groups-

In a medical environment where few groups of patients are put under different medications and one needs to find out which medicine is working better
In a production unit there might be two ways of developing a particular product and one needs to decipher which of the process is better
When exams are conducted in two or more colleges and it needs to be decided which college got the better result

Types of ANOVA

There are mainly two types of ANOVA. They are-

One-way ANOVA
Two-way ANOVA

One-way ANOVA

One-way or unidirectional ANOVA is a tool that is used for the comparison of two means from two unrelated or independent groups with the use of the F-distribution. After comparison, if the result generates a null hypothesis, then the groups are equal. On the other hand, if there is a significant result, then it means that the groups are unequal.

A one-way ANOVA assumes the following-

Independence- the dependent variable value for one observation is independent of any other observation’s value
Normalcy- the dependent variable value is distributed normally
Variance- one can compare the variance in different experiment groups
Continuous- the dependent variable is continuous and can be subdivided after being measured on a scale

The Process of One-way ANOVA

The one-way ANOVA has a null and alternative hypothesis. They are-

H₀– This is the null hypothesis. µ₁= µ₂= µ₃=…=µ_kIn this case, all the means of the population are equal
H₁– This is the alternative hypothesis. In this case, the mean of one population is different from the rest

When to use One-way ANOVA?

One-way ANOVA is used after collecting the data of one categorical independent variable and one quantitative dependent variable. One must note that the independent variable has at least three levels, i.e., three different groups or categories.

ANOVA’s One-way test enables one to understand whether the level of the independent variable makes any changes to the dependent variable.

For example-

An independent variable makes social media use and one assigns groups to low, medium, and high levels of social media use to know the difference in hours of breaks per night.

Limitations of One-way ANOVA

One-way ANOVA tells the user that at least two groups were different from each other but it does not tell which groups were different. If a test returns an f-statistic significant variable then one needs to run an ad hoc test to know which groups had the difference.

Two-way ANOVA

The Two-way ANOVA works with two independent variables that can have multiple levels. The two-way ANOVA is an extension of the one-way ANOVA. This type is used when one has a measurement variable and two nominal variables. The two-way ANOVA is useful when an experiment has a quantitative outcome and one has two categorical experimental variables.

ANOVA is a very helpful method to compare and analyze the groups of data.

Edureify with its online coding courses provides the most useful technical courses on Machine Learning like-

Azure Machine Learning
Machine Learning Algorithms
No-Code Machine Learning
The A-Z Statistics of Machine Learning
Hypothesis Testing in Machine Learning, and other important programming languages and tools.

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200+ learning hours
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Some FAQs on ANOVA-

1. Who developed ANOVA?

Ronald Fisher developed the ANOVA statistical formula.

2. What is the full form of ANOVA?

Analysis Variance is the full form of ANOVA.

3. What is ANOVA?

ANOVA is a statistical formula that is used for variance comparison across the means of different groups. ANOVA helps in determining the significance of a survey or experiment. It is a phenomenon that lets one decipher whether one needs to reject the null hypothesis or accept the alternate hypothesis.