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Statistics
Engineering & Social Sciences Program
Madrid, Spain
Dates: 1/16/25 - 6/4/25
Statistics
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Instruction in: English
Course Code: 13986
Transcript Source: Partner Institution
Course Details: Level 100
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Linear algebra, Calculus, Programming
DESCRIPTION
1. Descriptive statistics
1.1. Qualitative data vs quantitative data
1.2. Descriptive statistics for one variable
1.3. Descriptive statistics for two variables
2. Probability
2.1. Introduction to probability
2.2. Events and operations with events
2.3. Definition and properties of probability
2.4. Conditional probability and independence
2.5. The law of total probability
2.6. Bayes' theorem
3. Random variables
3.1. Concept of random variable
3.2. Discrete random variables
3.3. Continuous random variables
3.4. Characteristic measures of a random Variable
3.5. Independence of random variables
4. Distribution models
4.1. Binomial
4.2. Geometric
4.3. Poisson
4.4. Uniform (continuous)
4.5. Exponential
4.6. Normal (CLT)
5. Statistical inference
5.1. Introduction
5.2. Estimators and their sampling distributions
5.3. Confidence intervals
5.4. Hypothesis testing
6. Quality control
6.1. Introduction to quality control
6.2. Control charts for variables
6.3. Control charts for attributes
7. Linear regression
7.1. Introduction
7.2. Simple linear regression
7.3. Multiple linear regression
1.1. Qualitative data vs quantitative data
1.2. Descriptive statistics for one variable
1.3. Descriptive statistics for two variables
2. Probability
2.1. Introduction to probability
2.2. Events and operations with events
2.3. Definition and properties of probability
2.4. Conditional probability and independence
2.5. The law of total probability
2.6. Bayes' theorem
3. Random variables
3.1. Concept of random variable
3.2. Discrete random variables
3.3. Continuous random variables
3.4. Characteristic measures of a random Variable
3.5. Independence of random variables
4. Distribution models
4.1. Binomial
4.2. Geometric
4.3. Poisson
4.4. Uniform (continuous)
4.5. Exponential
4.6. Normal (CLT)
5. Statistical inference
5.1. Introduction
5.2. Estimators and their sampling distributions
5.3. Confidence intervals
5.4. Hypothesis testing
6. Quality control
6.1. Introduction to quality control
6.2. Control charts for variables
6.3. Control charts for attributes
7. Linear regression
7.1. Introduction
7.2. Simple linear regression
7.3. Multiple linear regression