## Understanding the correlation and regression analysis values

By Riya Jain and Priya Chetty on August 23, 2021

This article explains the different correlation and regression analysis values that are generated after conducting the tests. Their meaning, importance and how to interpret them are explained here.

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## Why conduct a multicollinearity test in econometrics?

By Riya Jain and Priya Chetty on March 19, 2020

A multicollinearity test helps to diagnose the presence of multicollinearity in a model. Multicollinearity refers to a state wherein there exists inter-association or inter-relation between two or more independent variables.

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## How to work with a mediating variable in a regression analysis?

By Riya Jain and Priya Chetty on March 5, 2020

When the linkage between 2 variables exists through a middle variable. This middle variable is referred to as a mediating variable.

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## How to improve the correlation between the variables?

By Riya Jain and Priya Chetty on February 27, 2020

Correlation refers to the extent of a relationship between the variables. In order to determine this relationship, it is essential to establish a correlation between the variables.

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## How to interpret results from the correlation test?

By Riya Jain and Priya Chetty on September 19, 2019

Correlation is a statistical measure that helps in determining the extent of the relationship between two or more variables or factors. For example, growth in crime is positively related to growth in the sale of guns. Growth in obesity is positively correlated to growth in consumption of junk food. However, growth in environmental degradation is […]

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## How to test time series autocorrelation in STATA?

By Rashmi Sajwan and Priya Chetty on October 22, 2018

This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.

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## Performing Canonical Correlation Analysis (CCA)

By Priya Chetty on January 10, 2018

Until recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set.Â Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets.

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