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Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
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VP Innovation & Strategic Partnerships, The Logit Group
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Before we can delve into quantile regression, let us first understand what exactly a quantile is. A quantile is a point in a distribution that relates to its rank order of values within that distribution. The middle value within the sample is known as the median, or middle quantile.
Quantile regression allows us to quantify the association of explanatory variables with a conditional quantile of a dependent variable even when the data does not satisfy the assumption of normality (when the distribution of sample means across independent samples is normal). Quantile regression is generally used when the data does not satisfy the assumptions for linear regression.
Create an actionable feedback collection process.
Quantile regression is often used for the following reasons:
The quantile regression model equation is as follows:
Where,
p = the number of regressor variables
n = the number of data points
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These are a few advantages of quantile regression:
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A quantile refers to a point in a distribution that relates to its rank order of values within that distribution.
Quantile regression is a kind of regression analysis used in statistics to estimate the conditional median of the response variable.
Quantile regression is generally used when the data does not satisfy the assumptions of linear regression.
A few strengths of quantile regression are;
The key differences between quantile regression and linear regression are;
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