Instance: 2018/2019 - 2S
Study cycles/ courses
||No. of students
||Syllabus since 2009/2010
Teaching - Hours
This course unit aims to provide students with an integrated vision of the basic concepts and techniques of Statistics.
Learning outcomes and competences
At the end of this course unit, students should be capable of:
-using methods to explore, summarize and present data;
- using statistical inference methods.
- INTRODUCTION TO STATISTICS: Data and Observations. Populations and Samples. Statistical Method.
- DESCRIPTIVE STATISTICS: Types of Data and Measure Scales. Summarizing Categorical, Quantitative and Bivariate Data.
- PROBABILITIES: Random Experiments. Sampling Spaces and Events. Probability, Conditional Probability and Independence. Total Probability and Bayes Theorem.
- RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS: Random Variables. Discrete and Continous Random Variables. Mass, Density and Cumulative Probability Functions. Population Parameters. Joint Probability Distributions. Covariance and Correlation. Transformed Variables.
- MAIN DISCRETE AND CONTINUOS DISTRIBUTIONS: Binomial, Negative Binomial, Hypergeometric and Poisson Distributions. Uniform, Exponential and Normal Distributions. Chi-square, t and F Distributions.
- SAMPLING AND SAMPLING DISTRIBUTIONS: Sampling and Random Sampling. Sampling Distributions. Central Limit Theorem. Generation of Random Variables.
- ESTIMATION AND CONFIDENCE INTERVALS: Estimators and Estimates. Confidence Interval. Confidence Intervals for Expected Values, Variances and Proportions. Sample Size Determination.
- STATISTICAL HYPOTHESIS TESTING: Statistical Inference Logic and Scope. Hypothesis Testing Methodology. Significance Level and Statistical Power (Type I and Type II Errors). Relationship between Hypothesis Testing and Confidence Intervals. Hypothesis Testing concerning Expected Values, Variances and Proportions.
- INTRODUCTION TO REGRESSION ANALYSIS: Simple Linear Regression Model. Regressin Parameters Estimation (OLS). Inferences about regression Parameters. Predictions based on the Simple Linear Regression Model. Regression Assumptions.
- INTRODUCTION TO DATA MINING: Main Concepts and Applications. Data Analysis with RapidMiner.
A. Miguel Gomes e José F. Oliveira; Estatística - Apontamentos de Apoio às Aulas, 2018
Rui Campos Guimarães e José António Sarsfield Cabral; Estatística, 2ª edição
, Verlag Dashofer, 2011. ISBN: 978-989--642-108-3
Devore Jay L.; Modern mathematical statistics with applications
. ISBN: 978-1-4614-0390-6
Nathan Tintle, Beth L. Chance, George W. Cobb, Allan J. Rossman, Soma Roy, Todd Swanson, Jill VanderStoep; Introduction to Statistical Investigations, Wiley, 2015. ISBN: 978-1-119-15430-3
Wonnacott Thomas H. 1935-; Introductory statistics
. ISBN: 0-471-51733-X
Teaching methods and learning activities
Theoretical classes: presentation of the course unit themes followed by examples and problem solving Theoretical-practical classes: problem solving and clarification of doubts
Physical sciences > Mathematics > Probability theory
Physical sciences > Mathematics > Statistics
Type of assessment
Distributed evaluation with final exam
Amount of time allocated to each course unit
|Frequência das aulas
Eligibility for exams
Admission criteria set according to Article 4 of General Evaluation Rules of FEUP.
Calculation formula of final grade
The final mark (CF) will be obtained by the following formula:
CF = 0.25 AD + 0.75 EF
AD - Quizzes:
- 6 quizzes (pratical classes);
- the quizzes mark (AD) is obtained by the average of the best 4 marks achieved by each student.
EF - Final Exam
- written exam.
To pass this course, apart from a final grade no less than 10, is required a minimum grade of 7 in the final exam.
Special assessment (TE, DA, ...)
Special evaluations will be made by a written exam.
Students may choose between:
- improving simultaneously components Quizzes (AD) and Final Exam (EF);
- improving only component Final Exam (FE).