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There different instruments to collect primary data and the most widely used is the questionnaire in a survey method. Correlation and regression tests are two of the basic statistical tools that are widely applied to analyze data.

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By Avishek Majumder & Abhinash Jena on October 30, 2018 No Comments

Epidemiology is a branch of study that predicts the occurrences and patterns of diseases in different groups of the population. It helps in assessing the reason and factors behind the occurrence of a disease. Epidemiological information helps plan and strategies to prevent and manage epidemic diseases or illness.

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By Priya Chetty on September 18, 2018 No Comments

Receiver Operating Curve (ROC) is an extension of such classifications. Performance of binary classifier system in the case of ROC analysis can be tested.

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By Prateek Sharma & Priya Chetty on July 16, 2018 2 Comments

K- Nearest Neighbor, popular as K-Nearest Neighbor (KNN), is an algorithm that helps to assess the properties of a new variable with the help of the properties of existing variables. KNN is applicable in classification as well as regression predictive problems.

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By Priya Chetty on June 15, 2018 1 Comment

Cluster analysis serves as an extension to qualitative data representation through data visualisation. It is an exploratory technique for visualising patterns in a study by grouping sources or nodes.

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This article attempts to empirically examine the relationship between FDI inflows and Total Factor Productivity (TFP). There are different types of factors of production; single or partial factor profitability.

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By Prateek Sharma & Priya Chetty on May 4, 2018 2 Comments

Instrumental variable is a third variable that estimates causal relationships in the regression analysis when an endogenous variable is present. Instrumental variables are useful when the independent variable in the regression model correlates with the error term in the model.

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By Prateek Sharma & Priya Chetty on April 3, 2018 1 Comment

In statistics, to increase the prediction accuracy and interpret-ability of the model, LASSO (Least Absolute Shrinkage and Selection Operator) is extremely popular. It is a regression procedure that involves selection and regularisation and was developed in 1989. Lasso regression is an extension of linear regression that uses shrinkage. The lasso imposes a constraint on the sum of the absolute values of the model parameters. Here the sum has a specific constant as an upper bound.

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