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B002657 - STATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Further information
Type of Assessment
Course program
Academic Year 2021-22
Coorte 2021 - Second Cycle Degree in WILDLIFE SCIENCES AND MANAGEMENT
Course year
First year - Second Semester
Belonging Department
Agriculture, Food, Environment and Forestry (DAGRI)
Course Type
Single education field course
Scientific Area
SECS-S/01 - STATISTICS
Credits
6
Teaching Hours
48
Teaching Term
28/02/2022 ⇒ 10/06/2022
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Mutuality
Course teached as:
B026853 - STATISTICA PER LE BIOTECNOLOGIE
Second Cycle Degree in BIOTECHNOLOGY FOR ENVIRONMENTAL MANAGEMENT AND SUSTAINABLE AGRICULTURE (BIO-EMSA)
B026853 - STATISTICA PER LE BIOTECNOLOGIE
Second Cycle Degree in BIOTECHNOLOGY FOR ENVIRONMENTAL MANAGEMENT AND SUSTAINABLE AGRICULTURE (BIO-EMSA)
Teaching Language
Italian
Course Content
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.
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.
Suggested readings (Search our library's catalogue)
“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.
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.
Teaching Methods
Contact hours for:
Classroom Lectures: 24 hours
E-learning: 24 hours
Classroom Lectures: 24 hours
E-learning: 24 hours
Further information
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.
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.