Universidad Carlos III de Madrid
Area of Study
Taught In English
Course Level Recommendations
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units4
Hours & Credits
Statistics (251 - 15328)
Study: Bachelor in Aerospace Engineering
Semester 2/Spring Semester
1ST Year Course/Lower Division
Competences and skills that will be acquired and learning results:
Once successfully having studied this subject, the students should be able to:
- Analyze problems involving random phenomena
- Define populations for a statistical study
- Build Hypothesis about a distribution
- Test hypothesis about the paramters of the chosen model
- Evaluate how well does the model fit to reality
- Understand the limitations of the methods that have been studied and the conditions under which they lead to wrong conclusions Ability to work in a team.
Description of contents:
BLOCK I: PROBABILITY
1. Introduction to Probability
1.2 Random phenomena
1.3 Definition of probability and properties
1.4 Assessment of probabilities in practice
1.5 Conditional probability
1.6 Bayes Theorem
2. Random variables
2.1 Definition of random variable
2.2 Discrete random variables
2.3 Continuous random variables
2.4 Characteristic features of a random variable
2.5 Independence of random variables
BLOCK II: PARAMETRIC MODELS AND INFERENCE
3. Distribution models
3.1 Binomial distribution
3.2 Geometric distribution
3.3 Poisson distribution
3.4 Uniform distribution (continuous)
3.5 Exponential distribution
3.6 Normal distribution (with CLT)
4. Statistical Inference
4.2 Estimators and their distributions
4.3 Confidence Intervals
4.4 Hypothesis testing
4.5 Particualr tests on a single sample
4.6 Comparison of two populations
BLOCK III: APPLICATIONS
5. Quality control
5.1 Introduction, control charts
5.2 Variables control charts, the X-bar chart
5.3 Attributes control charts, the p and np charts
6. Linear regression
6.2 Simple linear regression
6.3 Multiple linear regression
Learning activities and methodology:
- Lectures: introducing the theoretical concepts and developments with examples, 2.2 ECTS
- Problem solving sessions: 2.2 ECTS
- Computer (practical) sessions: 0.6 ECTS -- 4 SESSIONS
- Evaluation sessions (continuous evaluation and final exam): 1 ECTS
There will be continuous evaluation by means of two partial examinations. There will be some questions about the computer sessions at those exams.
If the grade obtained at the continuous evaluation is higher than 5, the student should not attend the final exam and his/her final grade will be the grade of the cotinuous evaluation.
If the grade obtained at the continuous evaluation is lower than 5, the student will have to attend the final exam. For those students, the final grade will be computed giving a 40% weight to the partial examinations, and a 60% weight to the grade at the final exam.
The grade for the students attending the extraordinary examination will be the grade obtained at such exam.
MONTGOMERY, D.C., RUNGER, G.C.. Applied Statistics and Probability for Engineers. John Wiley & Sons. 2003
Navidi, W.. Statistics for Engineers and Scientists. McGraw-Hill. 2006
GUTTMAN, L., WILKS, S.S., HUNTER, J.S.. Introductory Engineering Statistics. Wiley, 1992.
Courses and course hours of instruction are subject to change.
ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.