UE Statistics and probability for life sciences - STA331 -

This course aims at acquiring the basics of descriptive and inferential statistics. It will deal in particular with methods of point estimation, estimation by confidence interval, and hypothesis testing in a parametric framework. The teaching in the form of lectures/tutorial is completed by practical work sessions on computer aimed at familiarising students with the handling of the R software for statistics.

Basic knolwledge in mathematics: calculus, fractions, powers and percentages, sequence of real numbers, etc.

• Load, explore, and summarize graphically a set of data;
• Understand what is a probabilistic model, discrete or continuous;
• Perform simple computations on some basic models, in particular binomial and Gaussian;
• Identify a probabilistic model from a practical situation;
• Compute confidence interval estimates for proportions, means, and variances;
• Formulate hypotheses, compute tests statistics and p-values, interpret results and make practical decisions;
• Perform analyses of variance (anovas) under different models, and understand their outcomes;
• Adjust simple linear models, compute predicted values, test goodness-of fit by an anova;
• Use the R statistical software