Description

Theory
1

Theory/Practice
1

Laboratory
1.5

Instructors

Helena Brás

Contents

CP1. Introduction to Python.
 

CP2. Descriptive Statistics: Data types; characterization and representation; descriptive measures.
 

CP3. Discrete and Continuous Probability Distributions: Random variables; probability, density and distribution functions; independence, covariance and correlation; discrete (Binomial, Poisson) and continuous (Uniform, Exponential and Normal) distributions.
 

CP4. Sampling Distributions and Confidence Intervals (CI): Sampling, central limit theorem, sampling distributions and CI for means and proportions.
 

CP5. Hypothesis Testing (HT): Specification, significance level and power; tests for means and proportions.
 

CP6. Simple Linear Regression: Estimation and inference (CI and HT) of regression parameters by least squares.

Learning Outcomes

By the end of this course, the student should be able to:
CO1: Select probabilistic models to solve statistical problems.
CO2: Select statistical models associated with the mean and proportion of random samples.
CO3: Explain decisions based on parametric statistical inference.
CO4: Discuss the results of the estimates.
CO5: Estimate results using linear regression models and decide based on inference about the regression model coefficients.
CO6: Use statistical methods to solve simple and specific problems in Computer Engineering.
CO7: Organize teamwork to achieve objectives