The value of data is now recognized in numerous areas of application, including in Sports Science.
This Curricular Unit starts by introducing the introductory concepts of Biostatistics, followed by the presentation of various types of tables, graphs and descriptive measures that can be use to organize and characterize the data.
Follows the Inferential Statistics, which aims to infer about the population, based on knowledge of the sample data, encompassing the point and interval estimation of parameters and the testing of hypotheses about them.
It is also analyzed, graphically and analytically, the correlation between quantitative variables and are developed simple and multiple linear regression models, which are very useful for predictive purposes.
The CU has a theoretical-practical character, including several practical applications in Sports Sciences, using data analysis software.
Semestral
S1: Introduction to Biostatistics
Basic concepts
Statistical method steps
Variables and their classification
Data collection methods
S2: Descriptive Statistics
Location, dispersion, asymmetry and kurtosis measures
Frequency and contingency tables
Graphical representations
S3: Inferential Statistics
Introduction to Inferential Statistics
Probabilities and random variables
Point and interval estimation
Parametric and nonparametric hypothesis tests
Hypothesis testing for the population mean
Hypothesis tests to compare population means
Chi-square independence test
S4: Correlation and Linear Regression
Linear correlation
Simple linear regression
Multiple linear regression
Inferences about linear regression models
Assessment of the quality of adjustment
Model diagnosis
Use of models for predictive purposes
S5: Obtaining, organizing and analyzing data using software jamovi
S6: Preparation of reports, scientific papers and presentations
At the end of this course unit, students should be able to:
LO1: Distinguish fundamental concepts of Biostatistics;
LO2: Represent data through tables, graphs and descriptive measures;
LO3: Perform confidence intervals and hypothesis testing;
LO4: Analyze correlations and fit linear regression models to data;
LO5: Use software jamovi for statistical data analysis;
LO6: Report statistical studies.
Mandatory
The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises, with and without the use of a computer.
Knowledge evaluation is made by continuous evaluation or by written test of the final exam, applying the ISMAT Knowledge Evaluation Regulation.
The student is approved if he/she obtains a classification equal to or greater than 9.5 values and an attendance equal to or greater than 75%, except for the exceptions provided for in the ISMAT Evaluation Regulation.
The continuous evaluation consists of:
- Exercises: 20%;
- Group Work: 40%;
- Written test: 40%.
The final classification is obtained through the weighted arithmetic average of the classifications obtained in the three evaluations. It should be noted that any rounding to the units, during the continuous evaluation process, will only occur at the end.
Português
Não
Morrow, J., Jackson, A.; Disch, J.; Mood, D. (2000). Measurement and evaluation in Human Performance. (2ª ed). Champaign, USA: Human Kinetics.
Vidal, P. (2005). Estatística Prática para as Ciências da Saúde. Lisboa: Lidel.
Vieira, S. (2015). Introdução à Bioestatística. (5ª ed.). Rio de Janeiro: Elsevier.
