Métodos Estatísticos

Fundamental notions of set algebra, differential and integral calculus.

Theoretical classes primarily utilize an expository and inquisitive method, introducing concepts and techniques accompanied by examples associated with real-world data applicable to the field of engineering, whenever possible. In practical classes, students must solve the proposed exercises under the guidance of the professor. Computer tools (e.g., Excel/R) will be used whenever justified. Student support is provided through clarification of theoretical doubts and problem-solving during classes and office hours.

Knowledge: The fundamental concepts of Probability and Statistics necessary for the study, analysis, and interpretation of data, and for the study and use of models with applications in the areas of industrial engineering, are presented.
Generic Skills: Application of knowledge and understanding. Critical thinking and interpretation of results. Communication. Self-learning. Ability to work in a group, developing interpersonal relationships.
Specific Skills: Learning the main concepts of Probability and Statistics, for a follow-up and understanding of the topics to be covered in this curricular unit, namely, learning and knowing how to use the methods of data interpretation and analysis and the probabilistic models that constitute the basis of statistical inference. Learning to use and interpret basic tools of statistical inference.

1. Introduction to Probability Theory
Definitions of Probability. Properties. Conditional probability and independence. Total probability theorem and Bayes’ theorem.
2. Random Variables
Definition. Density function and distribution function. Discrete and continuous random variables. Parameters: mean, moments, median, and quantiles. Variance and standard deviation. Properties of the mean and variance. Discrete distributions: binomial, hypergeometric, and Poisson. Continuous distributions: uniform, exponential, and normal. Reference to the t-Student, Chi-square, and F-Snedecor distributions.
3. Introduction to Statistical Inference
Notions of sampling. Random sample. Sampling distributions. Point and interval estimation: confidence intervals for the expected value, for the proportion, for the variance of a normal population, for the difference of expected values, for the difference of proportions, and for the quotient of variances of normal populations. Hypothesis testing

Assessment Methods:

The student can choose between continuous evaluation or evaluation by final exam.

• Continuous evaluation - consists of a writen test and an assessment of regular class atendance. The student can
get approval if the sum of the previous components is equal to or greater than 9.5 values.

The writen test will be graded with 12 points and will happen in the last week of classes.

The evaluation of regular class atendance will be materialized through problems proposed during the
semester for a total of 8 points. The dates of realization of these problems will not be announced in
advance to students.

A minimum score of 50% is required for each assessment component. Students who do not meet this requirement must take a final exam.

• Final Exam - consists of conducting an exam quoted for 20 points, which will evaluate all chapters of the
program contents. Students who have not passed the continuous assessment or who have not chosen continuous assessment and those who are allowed to improve their rating may access the exam.

NAO