#### Multivariate

#### Data Science

Multivariate: The study of more than two variables is nothing but multivariate analysis. This analysis is used to understand the effect of variables on the responses.

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.

#### Eigenvectors

#### Data Science

: Eigenvectors are basically used to understand linear transformations. These are calculated for a correlation or a covariance matrix.

Eigenvectors are a special set of vectors associated with a linear system of equations (i.e., a matrix equation) that are sometimes also known as characteristic vectors, proper vectors, or latent vectors.

#### Eigenvalue

#### Data Science

Eigenvalue: Eigenvalues can be referred to as the strength of the transformation or the factor by which the compression occurs in the direction of eigenvectors.

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#### T-Test

#### Data Science

T-Tests are a type of hypothesis tests, by which you can compare means. Each test that you perform on your sample data, brings down your sample data to a single value i.e. T-value.

A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics. Calculating a t-test requires three key data values.

#### What are different types of Hypothesis Testing?

#### Data Science

What are different types of Hypothesis Testing?

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#### T-test

#### Data Science

T-test: T-test is used when the standard deviation is unknown and the sample size is comparatively small.

A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics. Calculating a t-test requires three key data values.

#### Chi-Square Test for Independence:

#### Data Science

Chi-Square Test for Independence: These tests are used to find out the significance of the association between categorical variables in the population sample.

Chi-Square Test of Independence. The Chi-Square test of independence is used to determine if there is a significant relationship between two nominal (categorical) variables. The frequency of each category for one nominal variable is compared across the categories of the second nominal variable.

#### ANOVA

#### Data Science

Analysis of Variance (ANOVA): This kind of hypothesis testing is used to analyze differences between the means in various groups. This test is often used similarly to a T-test but, is used for more than two groups.

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#### Welch’s T-test

#### Data Science

Welch’s T-test: This test is used to find out the test for equality of means between two population samples.

In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the hypothesis that two populations have equal means.

#### How to represent a Bayesian Network in the form of Markov Random Fields (MRF)?

#### Data Science

How to represent a Bayesian Network in the form of Markov Random Fields (MRF)?

Thank you for asking this question How to represent a Bayesian Network in the form of Markov Random Fields (MRF)?".