Elements of descriptive statistics with R. The calculus of probabilities.
Descriptive statistics.
Inferential statistics.
Hypothesis testing.
Analysis of proportions.
Fitting probability models to frequency data.
The normal distribution and Inference for a normal population.
Comparison of two averages.
Correlation between numerical variables.
Regression.
“Analisi statistica dei dati biologici” Michael C. Whitlock, Dolph Schluter 2nd Edition
Learning Objectives
Knowledge acquired:
basic elements of statistics; linear and regression models for univariate responses; foundations of experimental design.
Competence acquired:
recognizing the nature of variables investigated during the study of a phenomenon; evaluation of critical features characterizing a designed experiment; selection of suitable statistical techniques to perform the analysis of experimental results.
Skills acquired (at the end of the course):
1. assessment of raw data quality by means of suitable summaries; summarizing the key features of the investigated phenomenon;
2. data analysis using the R software;
3. fitting linear models;
4. using statistical principia in designing simple experiments.
Prerequisites
Courses to be used as requirements (required and/or recommended)
Courses required: none
Courses recommended: basic calculus.
Frequency of lectures, practice and lab, although non compulsory, is strongly recommended
Type of Assessment
Written test on subjects of lectures, webinars, laboratory assignments and homework.
Course program
Elements of descriptive statistics with R. The calculus of probabilities.
Descriptive statistics.
Inferential statistics.
Hypothesis testing.
Analysis of proportions.
Fitting probability models to frequency data.
The normal distribution and Inference for a normal population.
Comparison of two averages.
Correlation between numerical variables.
Regression.