Experimental planning and biostatistics in biotechnology
A.Y. 2020/2021
Learning objectives
The aim of the course is to provide fundamental knowledge and practical techniques to analyse biotechnological data applying descriptive statistics, inferential statistics and hypothesis testing to understand and perform the most appropriate analysis. The course also aims to provide the basic tool to generate and analyse common experimental designs in biotechnology. Besides theoretical classes, close attention will be given to computer sessions.
Expected learning outcomes
Students will acquire the following skills: 1) organize, summarize and represent biotechnological data; 2) define the appropriate experimental design; 3) use the appropriate standard errors and confidence intervals; 4) realize the appropriate statistical test on data; 5) understand and comment the output of a statistical software.
Lesson period: First semester (In case of multiple editions, please check the period, as it may vary)
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Teaching methods
The lessons will be held mainly on the Microsoft Teams platform in synchronization. Given the contents, it is recommended to follow the course step by step. The lessons will be recorded and available to students on the same platform. Three 'live', full frontal lessons will be offered: the first will serve as introduction to the course. The remaining two 'live' lessons will take place in the middle and near the end of the course and will provide opportunities to work on exercises related to course topics. To participate in these 'live' lessons, a reservation with a special app will be required. For those who cannot attend, additional materials in the form of recorded classes will be made available. 'Live' participation in activities will be voluntary and will have no effect on the final grade.
All information about 'live' lessons, as well as all the didactic material of the course and notices about any updates in course organization will be published on the Ariel teaching site.
The course will start 2020, Monday 28th in classroom C11. Please write an email to the teacher if you need more information.
Programme and reference material:
The program and reference material will not be changed.
Methods of learning verification and evaluation criteria
The exams will take place orally using the Microsoft Teams platform and will consist of theoretical questions, simple practical exercises on excel and a commentary on the results of an analysis carried out with statistical software in order to ascertain:
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of a statistical analysis by commenting on an analytical output of statistical software used during the course and the ability to plan an experiment.
Each of the three above-mentioned criteria (a,b,c) will be scored from 1 to 10 points. The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
The lessons will be held mainly on the Microsoft Teams platform in synchronization. Given the contents, it is recommended to follow the course step by step. The lessons will be recorded and available to students on the same platform. Three 'live', full frontal lessons will be offered: the first will serve as introduction to the course. The remaining two 'live' lessons will take place in the middle and near the end of the course and will provide opportunities to work on exercises related to course topics. To participate in these 'live' lessons, a reservation with a special app will be required. For those who cannot attend, additional materials in the form of recorded classes will be made available. 'Live' participation in activities will be voluntary and will have no effect on the final grade.
All information about 'live' lessons, as well as all the didactic material of the course and notices about any updates in course organization will be published on the Ariel teaching site.
The course will start 2020, Monday 28th in classroom C11. Please write an email to the teacher if you need more information.
Programme and reference material:
The program and reference material will not be changed.
Methods of learning verification and evaluation criteria
The exams will take place orally using the Microsoft Teams platform and will consist of theoretical questions, simple practical exercises on excel and a commentary on the results of an analysis carried out with statistical software in order to ascertain:
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of a statistical analysis by commenting on an analytical output of statistical software used during the course and the ability to plan an experiment.
Each of the three above-mentioned criteria (a,b,c) will be scored from 1 to 10 points. The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
Course syllabus
Program
Introduction: descriptive and inferential statistics. Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy. Theoretical class 2hrs
Data visualization: Absolute, relative and cumulative frequency tables. Representing the frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplot, line chart, maps. Theoretical class 3 hrs - practical class 2 hrs
Describing Data: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation. Theoretical class 3 hrs - practical class 3 hrs
Estimate with uncertainty: sampling distribution, standard error and confidence interval. Theoretical class 2 hrs - practical class 2 hrs
Probability: basic rules, Venn diagrams, probability trees. Sum the odds. Independence and the product rule. Conditional probability. Theoretical class 2 hrs - practical class 1 hr
Hypothesis testing: null and alternative hypotheses and statistical significance, P-value. Hypothesis testing and confidence intervals. Error of first and second species. Theoretical class 3 hrs - practical class 1 hr
Analysis of proportions: binomial distribution. Estimate of the proportions: confidence interval and standard error of a proportion. Chi-square test and the goodness of fit. Poisson distribution. Contingency tables for the analysis of categorical variables and chi-square test for the analysis of contingency tables. Theoretical class 4 hrs - practical class 4 hrs
Normal distribution, formula, assumptions and properties. The central limit theorem. Normal approximation for binomial distribution. Theoretical class 3 hrs - practical class 2 hrs
Inference in a population with a normal distribution: t-distribution, assumptions and properties. t-test for one sample. Comparison between two means, paired comparison between means, comparing the means of two samples. Theoretical class 3 hrs - practical class 2 hrs
Comparisons between means of multiple groups: analysis of variance. Theoretical class 3 hrs - practical class 3 hrs
Measurements of relationship between variables: covariance, correlation and linear regression. Least squares. Linear regression. Theoretical class 4 hrs - practical class 2 hrs
Guidelines for designing experiments: the three basic principles of experimental design randomization, replication and blocking. Determining sample size. Theoretical class 4 hrs - practical class 2 hrs
Introduction: descriptive and inferential statistics. Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy. Theoretical class 2hrs
Data visualization: Absolute, relative and cumulative frequency tables. Representing the frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplot, line chart, maps. Theoretical class 3 hrs - practical class 2 hrs
Describing Data: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation. Theoretical class 3 hrs - practical class 3 hrs
Estimate with uncertainty: sampling distribution, standard error and confidence interval. Theoretical class 2 hrs - practical class 2 hrs
Probability: basic rules, Venn diagrams, probability trees. Sum the odds. Independence and the product rule. Conditional probability. Theoretical class 2 hrs - practical class 1 hr
Hypothesis testing: null and alternative hypotheses and statistical significance, P-value. Hypothesis testing and confidence intervals. Error of first and second species. Theoretical class 3 hrs - practical class 1 hr
Analysis of proportions: binomial distribution. Estimate of the proportions: confidence interval and standard error of a proportion. Chi-square test and the goodness of fit. Poisson distribution. Contingency tables for the analysis of categorical variables and chi-square test for the analysis of contingency tables. Theoretical class 4 hrs - practical class 4 hrs
Normal distribution, formula, assumptions and properties. The central limit theorem. Normal approximation for binomial distribution. Theoretical class 3 hrs - practical class 2 hrs
Inference in a population with a normal distribution: t-distribution, assumptions and properties. t-test for one sample. Comparison between two means, paired comparison between means, comparing the means of two samples. Theoretical class 3 hrs - practical class 2 hrs
Comparisons between means of multiple groups: analysis of variance. Theoretical class 3 hrs - practical class 3 hrs
Measurements of relationship between variables: covariance, correlation and linear regression. Least squares. Linear regression. Theoretical class 4 hrs - practical class 2 hrs
Guidelines for designing experiments: the three basic principles of experimental design randomization, replication and blocking. Determining sample size. Theoretical class 4 hrs - practical class 2 hrs
Prerequisites for admission
None
Teaching methods
Besides theoretical classes (4.5 CFU, 32 hours), close attention will be paid to computer sessions (1.5 CFU, 24 hours). Biostatistics will be presented with a practical approach emphasizing the rationale of statistical theory and methods rather than mathematical proofs and formalisms. Each theoretical lecture will be combined with practical applications and exercises using simple spreadsheets to analyze experimental data. Besides theoretical classes, there will also be intensive computer sessions
Students can choose to attend this course during the first year (I semester) or the second year (I semester). As the skills acquired from this teaching are useful for experimental internship activities, students are warmly encouraged to attend the course in the first year (I semester).
Students can choose to attend this course during the first year (I semester) or the second year (I semester). As the skills acquired from this teaching are useful for experimental internship activities, students are warmly encouraged to attend the course in the first year (I semester).
Teaching Resources
Slides of any lecture, exercises, datasets, procedures for data analysis and bibliographic material will be provided by the teacher through the Ariel online platform (www.ariel.unimi.it).
The core text is: The analysis of Biological data - Second edition. Whitlock M.C. Schluter D. Ed. By W.H. Freeman and company
Other reference books will be suggested during the course.
The core text is: The analysis of Biological data - Second edition. Whitlock M.C. Schluter D. Ed. By W.H. Freeman and company
Other reference books will be suggested during the course.
Assessment methods and Criteria
The exam consists of a written and a practical examination and an oral interrogation. The written part consists of 6 questions (1 multiple choice question, 2 open theoretical questions; 2 practical problems; 1 statistical analysis to comment on, and a computer session with 1 dataset to analyze. The biostatistical test will assess the ability to organize, summarize, and represent biotechnological data by choosing the appropriate experimental methodologies and statistical tests. The oral interrogation will be devoted to commenting on the results of the written and practical examination and to the presentation of an experimental plan. The final score will be the summation of the points from 1-10 of the three different parts aimed at ascertaining:
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, a compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
(a) your knowledge and ability to understand the topics of the course as well as mastery of the specific language related to the use of statistical techniques and the ability to present topics in a clear and orderly manner
(b) your ability to apply knowledge and understanding through the analysis of a dataset or the execution and discussion of a statistical problem using excel
(c) your ability to understand and interpret the results of statistical analysis by commenting on an analytical output of statistical software used during the course and your ability in organize an experimental plan.
The final evaluation is based on a total of 30 points.
Activities carried out by students during the course, such as commentaries on analyses, completion of exercises, a compilation of a dictionary of terms, etc. will also be taken into account when determining the final grade.
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 0
BIO/10 - BIOCHEMISTRY - University credits: 0
BIO/10 - BIOCHEMISTRY - University credits: 0
Computer room practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Crepaldi Paola
Educational website(s)
Professor(s)
Reception:
keeping an appointment by e-mail
Sezione di Zootecnica Agraria, 1st floor, Via Celoria 2