Você está em: Start > TGI38
Campus Map
Statistics II
| Code: | TGI38 | Sigla: | EST TT |
| Áreas Científicas | |
|---|---|
| Classificação | Área Científica |
| OFICIAL | Matemática |
Ocorrência: 2023/2024 - 2T
| Ativa? | Yes |
| Página Web: | https://moodle.ips.pt/2223/course/view.php?id=1746 |
| Unidade Responsável: | Departamento de Matemática |
| Curso/CE Responsável: | Industrial Management and Techinology |
Ciclos de Estudo/Cursos
| Sigla | Nº de Estudantes | Plano de Estudos | Anos Curriculares | Créditos UCN | Créditos ECTS | Horas de Contacto | Horas Totais |
|---|---|---|---|---|---|---|---|
| TGI | 57 | Plano de Estudos 2016 | 3 | - | 4 | 44 | 108 |
Docência - Responsabilidades
| Docente | Responsabilidade |
|---|---|
| José António da Conceição Palma |
Docência - Horas
| Theorethical and Practical : | 2,00 |
| E-Learning: | 2,00 |
| Type | Docente | Turmas | Horas |
|---|---|---|---|
| Theorethical and Practical | Totais | 1 | 2,00 |
| José António da Conceição Palma | 2,00 | ||
| E-Learning | Totais | 1 | 2,00 |
| José António da Conceição Palma | 2,00 |
Língua de trabalho
PortugueseObjetivos
The curricular unit contents are structured, regarding its suitability for the intended learning outcomes.Therefore, each subject approaches fundamentals concepts and practical applications by solving problems using the basic tools of Statistics to enable students to analyze certain phenomena of random nature, framed in the context of technology.
Resultados de aprendizagem e competências
1) Apply basic techniques to describe and summarize a data set through R.
2) Understand the meaning of statistical inference, random sample, point estimation, sample distributions, confidence intervals and hypothesis tests.
3) Know how to build and interpret point estimators and confidence intervals .
4) Identify and apply parametric hypothesis tests.
5) Identify and apply non -parametric hypothesis tests.
2) Understand the meaning of statistical inference, random sample, point estimation, sample distributions, confidence intervals and hypothesis tests.
3) Know how to build and interpret point estimators and confidence intervals .
4) Identify and apply parametric hypothesis tests.
5) Identify and apply non -parametric hypothesis tests.
Modo de trabalho
B-learningPré-requisitos (conhecimentos prévios) e co-requisitos (conhecimentos simultâneos)
Basic knowledge of Mathematics.Programa
1) Brief reference to descriptive statistics
(a) Basics of descriptive statistics and statistics inference.
(b) Data and variables. Classification of variables. Exploratory data analysis.
(c) Applications using R.
2) Introduction to Statistical Inference
(a) Random sample. Independent samples and paired samples.
(b) Point estimation. Sample distributions.
(c) Basics of confidence intervals and hypothesis tests.
3) Confidence intervals and parametric hypothesis tests
(a) Confidence intervals and hypothesis tests for mean and for the difference of means.
(b) Confidence intervals and hypothesis tests for the ratio of variances.
(c) Confidence intervals and hypothesis tests for the proportion and for the difference of proportions.
(d) Applications using R.
4) Non -parametric hypothesis tests
(a) Kolmogorov-Smirnov test. and Shapiro-Wilk test.
(b) Wilcoxon test and Mann-Whitney test.
(c) Chi-square goodness of fit and independence test.
(d) Applications using R.
(a) Basics of descriptive statistics and statistics inference.
(b) Data and variables. Classification of variables. Exploratory data analysis.
(c) Applications using R.
2) Introduction to Statistical Inference
(a) Random sample. Independent samples and paired samples.
(b) Point estimation. Sample distributions.
(c) Basics of confidence intervals and hypothesis tests.
3) Confidence intervals and parametric hypothesis tests
(a) Confidence intervals and hypothesis tests for mean and for the difference of means.
(b) Confidence intervals and hypothesis tests for the ratio of variances.
(c) Confidence intervals and hypothesis tests for the proportion and for the difference of proportions.
(d) Applications using R.
4) Non -parametric hypothesis tests
(a) Kolmogorov-Smirnov test. and Shapiro-Wilk test.
(b) Wilcoxon test and Mann-Whitney test.
(c) Chi-square goodness of fit and independence test.
(d) Applications using R.
Bibliografia Obrigatória
Murteira, B.; Antunes, M. ; Probabilidades e Estatística, Volume 2, Escolar Editora.Montgomery, D.; Runger, G.; Applied Statistics and Probability for Engineers, John Wiley & Sons.
Murteira, B.; Ribeiro, C. S.; Andrade e Silva, J. ; Introdução à Estatística. ISBN: McGraw-Hill.
Sheldon M. Ross ; Introduction to Probability and Statistics for Engineers and Scientist. ISBN: Elsevier/Academic Press
Bibliografia Complementar
André, J. ; Probabilidades e Estatística para Engenharia. ISBN: LidelSiegel, A.F. ; Statistics and data analysis : an introduction. ISBN: New York, John Wiley & Sons.
Watson, Billingsley, Croft, Huntsberger ; Statistics : for management and economics. ISBN: Boston, Allyn & Bacon.
Métodos de ensino e atividades de aprendizagem
Classroom lectures through a combination of lecture method and problem solving.
E-learning in the Moodle platform and Microsoft Teams, providing access to the contents of UC through several materials, promoting the holding of weekly activities.
Software
Software RTipo de avaliação
Distributed evaluation with final examComponentes de Avaliação
| Designation | Peso (%) |
|---|---|
| Teste | 100,00 |
| Total: | 100,00 |
Componentes de Ocupação
| Designation | Tempo (Horas) |
|---|---|
| Estudo autónomo | 86,00 |
| Frequência das aulas | 22,00 |
| Total: | 108,00 |
Obtenção de frequência
The approval in this curricular unit can be obtained through two assessment processes: Continuous Evaluation or Exam Evaluation.
Continuous Evaluation
2 tests.
Exam Evaluation
Evaluation by final examination.
Fórmula de cálculo da classificação final
Continuous EvaluationAssigning by MT the average of the tests (from zero to 20):
-If MF is greater than or equal to 10 and less than 18, the student is approved with a final mark equal to MF, provided that the classification in any of the tests was greater than or equal to 7.0 values.
- If the MF is equal to or greater than 18, the student must do an oral exam. The final grade is the average between the tests and the grade of the oral exam. If the student does not attend the oral exam, the final classification will be 17 points.
- If MF is less than 10, students will be able to recover the lowest test by performing a recovery test (in the same day of the first exam), provided that one of the tests is greater or equal than 7.0 values.
Exam Evaluation
-If the grade of the exam is greater than or equal to 10 and less than 18, the student is approved with a final mark equal to the grade of the exam.
- If the grade of the exam is equal to or greater than 18, the student must do an oral exam. The final grade is the average between the two grades. If the student does not attend the oral exam, the final classification will be 17 points.
- If the grade of the exam is equal to or greater than 18, the student must do an oral exam. The final grade is the average between the two grades. If the student does not attend the oral exam, the final classification will be 17 points.
Observações
All information about the course, including support materials, is available on the Moodle platform.
Copyright 1996-2024 © Instituto Politécnico de Setúbal - Escola Superior de Tecnologia de Setúbal
I Termos e Condições
I Acessibilidade
I Índice A-Z
Página gerada em: 2024-11-23 às 11:29:48
Página gerada em: 2024-11-23 às 11:29:48