Lecture

Oxygen Consumption Analysis

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In courses (2)

BIO-378: Physiology lab I

Le TP de physiologie introduit les approches expérimentales du domaine biomédical, avec les montages de mesure, les capteurs, le conditionnement des signaux, l'acquisition et traitement de données. Les résultats physiologiques finaux illustrent le contenu ...

BIO-379: Physiology lab II

Description

This lecture focuses on determining oxygen consumption per cell using regression analysis, addressing the challenges of calculating average consumption and interpreting errors in cell counting and oxygen measurement. The lecture also explores the application of the Michaelis-Menten model to normalize oxygen consumption measurements and discusses the physiological implications of the results, highlighting the precision and potential sources of error in oxygen consumption experiments.

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Instructor

André Pexieder

Teaching & PhD Courses Physiology lab I BIO-378 The physiology laboratory introduce the experimental methods in the biomedical field, including experimental set-ups, transducers, signal conditioning, data acquisition and processing.The physiological end results illustrate the contents of the Physiologie par systèmes course. Physiology lab II BIO-379 The physiology laboratory introduce the experimental methods in the biomedical field, including experimental set-ups, transducer

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Related concepts (11)

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

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