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Regression Models for Life Sciences REG2 |
 Duration : 2.0 day(s) | |
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:: Course SummaryThis applied training session in statistics is aimed at all life sciences scientific staff designing experiments and analyzing data. This session covers the regression models used in life sciences: simple linear regression, multiple regression, logistic regression, case of several explanatory variables.:: Target AudienceThis applied training session in statistics is aimed at all life sciences scientific staff designing experiments and analyzing data.:: PrerequisiteParticipants should know the essential tools in statistics - descriptive statistics, both numerical (mean, standard deviation, standard error, etc.) and graphical (histogram, box-plot, scatter plot, etc.), hypothesis testing and confidence intervals - either by having attended the training session Fundamental Tools in Statistics or by possessing a similar background.:: Notes and Other InformationRegister up to 6 weeks in advance and save 15% on the training price. This discount is taken into account in the displayed price.
All our training sessions are available onsite. Contact us to learn more. | | |
:: Topics Covered
- Simple linear regression and multiple regression
- Difference between correlation and regression
- Graphical tools
- Models and types of models
- Assessing the fit of the model
- Residuals
- R-square
- Adjusted r-square
- PRESS
- Model selection
- Use of the model: explanatory or predictive purposes?
- Case studies
- Regression for qualitative variables
- Introduction to logistic regression for binary variables
- Use of logistic regression: control of confounders, risk prediction
- Case of a single explanatory variable: simple logistic regression
- Logistic, probit, logit functions
- Principles underlying the model estimation
- Assessing the goodness-of-fit of the model
- Interpretation of statistical software output: parameters, odds ratios, crossvalidation tables, ROC curves, etc.
- Case of several explanatory variables
- Model selection
- Application to various sampling schemes
- Introduction to logistic regression for ordinal variables
- Introduction to logistic regression for polytomous variables
:: Course ContentThis session covers the fundamentals of statistics in life sciences to design efficient studies, to make the most adequate analysis of the data and on the proper interpretation and reporting of results.
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Upcoming Public Sessions |
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Multivariate Data Analysis School |
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