Curricular Unit: Code: Applied Statistics 1093ESTA Year: Level: Course: Credits: 1 Undergraduate Computer Systems Engineering 5 ects Learning Period: Language of Instruction: Total Hours: Winter Semester Portuguese/English 65 Learning Outcomes of the Curricular Unit: The behaviour of random variables becomes essential. Considering this framework, this curricular unit aims the introduction and development of knowledge and techniques for data collection and analysis that are necessary for modelling random variables. At the end of the unit students stay with specific skills that allow them to describe, analyse and establish conclusions about uni and bi-varied data, calculate probabilities in simple and compound events, understand and use the main theoretical probability distribution models, estimate parameters within a given confidence interval and test hypotheses. Syllabus: 1 Basic concepts1.1 Objectives1.2 Population, sample, variables, measurement scales2 Descriptive Statistics2.1 Characterization of samples2.2 Measures of central tendency, partition, dispersion, skewness and kurtosis2.3 Linear regression model3 Probability Theory3.1 Random processes, sample spaces and events3.2 Definitions of probability3.3 Conditional probability and independent events3.4. Theorem of Total Probability and Bayes’ Theorem4. Random variables and probability distributions4.1 Discrete and continuous random variables4.2 Probability and distribution functions4.3 Expected value and variance4.4 Theoretical discrete and continuous models of probability distributions5 Confidence interval estimation5.1 For the mean and the difference between two means5.2 For a proportion and the difference between two proportions5.3 Sizing samples6 Hypothesis Tests6.1 Procedures involved and hypothesis tests Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives: Points 1 and 2 of the syllabus provide concepts and techniques for data collection and analysis and for summarize data, which allow describing, analysing and establishing simple conclusions about uni and bi-varied data. Points 3 and 4 introduce fundamental concepts concerning probability theory and random variables allowing the calculation of probabilities in simple and compound events and the understanding and application of main theoretical probability distribution models. Points 5 and 6 introduce the essential methods of statistical inference to parameters estimation and hypotheses testing. The contents are in accordance with the objectives of the course, comprising concepts and techniques involved in data collection, data analysis and inference, necessary to model random variables. Teaching Methodologies (Including Evaluation): The methodology of teaching and learning is expository, interrogative and demonstrative. Drawing on problem solving and oriented study geared to allow understanding and application of fundamental concepts and methods applied in descriptive statistics and statistical inference.The assessment includes:• Three written tests evaluation Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes: The proposed methodologies are consistent with the learning objectives of the unit, because they establish a close and continuous coordination between theoretical concepts, practical examples and problem solving. This approach enables the interpretation and practical application of statistical concepts and methods Reading:  Dekkin, F.M., Karaikamp, C., Lopuhaã, H.P., Meester, L. E. (2010). A Modern Introduction to Probability and Statistics. Understanding Why and How. Springer. Guimarães, R.C. e Cabral, J. A. S. (2010) Estatística. Verlag Dashofer. Montgomery, D.C., Runger, G. C. (2014) Applied Statistics and Probability for Engineers. Wiley.  Reis, E.; Melo, P; Andrade, R e Calapez, T. (2015) Estatística Aplicada. Vol. 1 e 2. Edições Sílabo. Lecturer (* Responsible): Isabel Abreu (iabreu@ufp.edu.pt)