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Statistics Applied to Journalism
Communication, Journalism & Media Studies Program
Madrid, Spain
Dates: 1/16/25 - 6/4/25
Statistics Applied to Journalism
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Other Subject Area: Journalism
Instruction in: English
Course Code: 14363
Transcript Source: Partner Institution
Course Details: Level 100
Recommended Semester Credits: 3
Contact Hours: 42
DESCRIPTION
1. Introduction.
1.1. Concept and uses of statistics.
1.2. Statistical terminology: populations, subpopulations, individuals and samples.
1.3. Types of variable.
2. Analysis of univariate data.
2.1. Representations and graphs of qualitative data.
2.2. Representations and graphs of quantitative data.
2.3. Numerical summary.
3. Analysis of bivariate data.
3.1. Representations and graphs of qualitative and discrete data.
3.2. Representations and graphs of continuous data: correlation and regression.
4. Probability and probability models.
4.1. Random experiment, simple space, elementary and composite events.
4.2. Properties of probability.
4.3. Conditional probability and its properties.
4.4. Random variables and their characteristics.
4.5. Bernoulli trials and related distributions.
4.6. The normal distribution.
5. Introduction to statistical inference.
5.1. Ideas and objectives.
5.2. Point estimation.
5.3. Interval estimation.
5.4. Fundamental concepts of hypothesis tests.
5.5. Tests for the mean in normal populations.
5.6. Tests for proportions.
1.1. Concept and uses of statistics.
1.2. Statistical terminology: populations, subpopulations, individuals and samples.
1.3. Types of variable.
2. Analysis of univariate data.
2.1. Representations and graphs of qualitative data.
2.2. Representations and graphs of quantitative data.
2.3. Numerical summary.
3. Analysis of bivariate data.
3.1. Representations and graphs of qualitative and discrete data.
3.2. Representations and graphs of continuous data: correlation and regression.
4. Probability and probability models.
4.1. Random experiment, simple space, elementary and composite events.
4.2. Properties of probability.
4.3. Conditional probability and its properties.
4.4. Random variables and their characteristics.
4.5. Bernoulli trials and related distributions.
4.6. The normal distribution.
5. Introduction to statistical inference.
5.1. Ideas and objectives.
5.2. Point estimation.
5.3. Interval estimation.
5.4. Fundamental concepts of hypothesis tests.
5.5. Tests for the mean in normal populations.
5.6. Tests for proportions.