bioinformatique@iric.ca

# curve fitting

## Introduction to Linear Regression

A data scientist's first goal is to find underlying relations within the variables of a dataset. Several statistical and machine learning methods can be used to discover such relations. Once uncovered, this information can be applied to everyday problems. For example, in clinical medicine, a predictive model based on clinical data can help clinicians guide a patient's treatment by offering insights that might not have otherwise been taken into account. Simple linear regression One of the most basic methods available to [...]

By |2017-08-03T14:39:54-04:00March 23, 2017|Categories: Data Analysis, Python||0 Comments

## Logistic regression and GTEx

Working with all sorts of data, it happens sometimes that we want to predict the value of a variable which is not numerical. For those cases, a logistic regression is appropriate. It is similar to a linear regression except that it deals with the fact that the dependent variable is categorical. Here is the formula for the linear regression, where we want to estimate the parameters beta (coefficients) that fit best our data : \begin{equation} Y_i = \beta_0 + \beta_1 X_i [...]

By |2017-04-29T17:44:14-04:00January 27, 2017|Categories: Biology, Data Analysis, Python||0 Comments

## SciPy and Logistic Regressions

Given a set of data points, we often want to see if there exists a satisfying relationship between them. Linear regressions can easily be visualized with Seaborn, a Python library that is meant for exploration and visualization rather than statistical analysis. As for logistic regressions, SciPy is a good tool when one does not have his or her own analysis script. Let's look at the optimize package                        from scipy.optimize import [...]

By |2017-04-29T16:58:35-04:00June 9, 2016|Categories: Data Analysis, Python||0 Comments