Curricular Unit:			Code:
Mathematics and Laboratory Statistics II			1100MEL2
Year:	Level:	Course:	Credits:
1	CTSP	Laboratory Analyses	4 ects
Learning Period:		Language of Instruction:	Total Hours:
Spring Semester		Portuguese/English	52
Learning Outcomes of the Curricular Unit:
This course aims to develop knowledge of statistical analysis techniques that are currently used in data analysis, in particular experimental results. The objectives/fundamental competences of the curricular unit determine that at the end of the semester the student should be able to: LO1: Characterize data sets and make their correct interpretation; LO2: Calculate probabilities and apply the concept of probability as a measure of uncertainty; LO3: Use statistical inference tools: confidence intervals and hypothesis tests; LO4: Perform simple tasks, autonomously, such as tabulating data, calculating frequency measurements and mastering some aspects of collecting, analysing and interpreting research data.
Syllabus:
1. Descriptive Statistics 1.1. Sampling characteristics for classified data 2. Correlation and Regression 2.1. Scatter diagram 2.2. Linear correlation coefficient 2.3. Simple linear regression 3. Elementary Theory of Probability 3.1 Random experience, sample space, event 3.2 Definitions of probability 3.3 Conditioned probability and independence of events 4. Probability distributions 4.1 Discrete and continuous random variables 4.2 Probability and distribution function 4.3 Expected value and variance 4.4 Theoretical models of probability distributions (binomial and normal) 5: Confidence intervals 5. Confidence Interval Estimation 5.1. Inference around the mean value 5.2. Inference around the variance 6: Hypothesis testing. 6.1. Procedure involved in hypothesis testing 6.2. Error analysis 6.3. Most common tests (mean value and difference of mean values; variance)
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
Laboratory Mathematics and Statistics II works as an auxiliary tool in laboratory analysis. The selected syllabus are statistical tools indispensable in the planning, interpretation and conclusion of a laboratory analysis. The syllabus was designed according to the learning objectives of the course. To achieve OA1 the student will have to acquire a satisfactory level of knowledge of the syllabus 1 and 2. The OA2 and OA3 will be achieved if the student acquires a satisfactory level of knowledge of the program contents 3 to 6. The OA4 will be reached if the student acquires a satisfactory level of knowledge for all syllabus.
Teaching Methodologies (Including Evaluation):
Oral presentation and explanation of the programmatic contents, with the support of audiovisual media. Appeal to the active participation of the students, through an interrogative methodology. Solving exercises including the use of the excel program. The minimum percentage of frequency in the theoretical-practical lessons of this subject is of 50%, according to UFP Pedagogical Regulation. Evaluation is periodic with two tests, each with an equal weight of 50%. Students who present a final classification inferior to 10 values in continuous evaluation are consider non-approved and do have to perform a final examination.
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The teaching-learning methodologies of this curricular unit were programmed to enhance the concepts learned. Thus, in an initial phase, the oral explanation allows the approach and understanding of the programmatic contents. Classes focus on active and participative methods aiming to maintain students' attention and develop their critical capacity. The resolution of exercises leads to a greater motivation of the students in the application and consolidation of the acquired knowledge. The appeal to the critical sense of the students in the analysis of the results enhances their capacity to interconnect the knowledge and to properly interpret the obtained results.
Reading:
[1] Daniel, W.; Cross, C. Biostatistics: A Foundation for Analysis in the Health Sciences, 11th ed., John Wiley and Sons, 2018. [2] DAWSON, Beth e Trapp, Robert G., Bioestatística Básica e Clínica, 3ª Ed. McGraw-Hill, 2003 [3] GOUVEIA de OLIVEIRA, A., Bioestatística Descodificada. 2.ª Edição. LIDEL, 2014. ISBN 978-989-752-044-0 [4] Guimarães, R.C. e Cabral, J. A. S., Estatística, Verlag Dashofer, 2010. [5] Lapponi, J.C. Estatística usando Excel, 4ª Edição, Campus - Elsevier, 2005. ISBN 85-352-1574-3