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Building Nonlinear Regression Models

We have talked about regression models in the context of linear regression models in the previous post. A nonlinear regression model is one that describes a nonlinear relationship between the dependent and the independent variables. Naturally, the equation of the...

The Assumptions in Linear Correlations

Given how simple Karl Pearson's Coefficient of Correlation is, the assumptions behind it are often forgotten. It is important to ensure that the assumptions hold true for your data, else the Pearson's Coefficient may be inappropriate. The assumptions and requirements...

Talking about Quartiles

Quartiles are a frequently used method to split the data and understand the spread. In general, data can be divided in various groupings such that an equal number of points are in each group. Such groups are formed by cutting at specific points called quantiles. If...

Measuring the Spread of Data

We have already seen how it is easier to describe data using a single measure of central tendency, such as, average. Now, take a look at the following data: The average score for both students is equal. However, John is consistent at everything, while Arun is...

Building a Multiple Linear Regression Model

Previously, we have seen situations where an outcome (the dependent variable) is based on a single input variable (independent variable). Sadly, real life is rarely as simple. Most outcomes in real situations are affected by multiple input variables. To...

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