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Statistics I
Communication, Journalism & Media Studies Program
Madrid, Spain
Dates: 1/16/20 - 5/30/20
Statistics I
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Instruction in: English
Transcript Source: TBD
Course Details: Level 100
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Calculus I
Linear Algebra
DESCRIPTION
In this course, the students study the foundations of probability and random variables. The programm is divided in 5 blocks:
* Probability
-Events
-Properties of probability
-Laplace rule
-Conditional probability and independence of events
-Bayes Theorem
*Random Variables
-Definition of random variable
-Dicrete random variables: probability and distribution functions
-Continuous random variables: density and distribution functions
-Moments of a random variable
-Transformation of random variables
*Probability models
-Bernouilli and Binomial
-Poisson
-Exponencial
-Normal
-Central Limit Theorem: Approximation of random variables
*Random Vectors
-Joint distribution
-Discrete and continupus random vectors: Joint probability, density and distribution functions
-Marginal distributions
-Conditional distributions, independence of events
-Moments of a random vector
-Transformations of renaom vectors
*Stochastic Processes
-Definition and classification of processes
-Distribution function
-Characteristic measures: Mean, Variance, Autocovariance, Autocorrelation
-Correlation between processes, independence, orthogonality
-Stationarity
-Ergodicity
* Probability
-Events
-Properties of probability
-Laplace rule
-Conditional probability and independence of events
-Bayes Theorem
*Random Variables
-Definition of random variable
-Dicrete random variables: probability and distribution functions
-Continuous random variables: density and distribution functions
-Moments of a random variable
-Transformation of random variables
*Probability models
-Bernouilli and Binomial
-Poisson
-Exponencial
-Normal
-Central Limit Theorem: Approximation of random variables
*Random Vectors
-Joint distribution
-Discrete and continupus random vectors: Joint probability, density and distribution functions
-Marginal distributions
-Conditional distributions, independence of events
-Moments of a random vector
-Transformations of renaom vectors
*Stochastic Processes
-Definition and classification of processes
-Distribution function
-Characteristic measures: Mean, Variance, Autocovariance, Autocorrelation
-Correlation between processes, independence, orthogonality
-Stationarity
-Ergodicity