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1.2.3. Variable Uniforme
Julio Toboso edited this page Jun 1, 2026
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Experimento consistente en elegir un valor dentro de un conjunto dado y finito de valores. No hay preferencia sobre ninguno de ellos, por lo tanto la probabilidad de que salga cualquiera de ellos es la misma.
Número de elementos
$L = 10$
| Valor variable (k) |
Función de Probabilidad |
||||
|---|---|---|---|---|---|
| 1 | 0.1 | 0.1 | 0.1 | -5.4 | 2.916 |
| 2 | 0.1 | 0.2 | 0.4 | -5.3 | 2.809 |
| 3 | 0.1 | 0.3 | 0.9 | -5.2 | 2.704 |
| 4 | 0.1 | 0.4 | 1.6 | -5.1 | 2.601 |
| 5 | 0.1 | 0.5 | 2.5 | -5 | 2.5 |
| 6 | 0.1 | 0.6 | 3.6 | -4.9 | 2.401 |
| 7 | 0.1 | 0.7 | 4.9 | -4.8 | 2.304 |
| 8 | 0.1 | 0.8 | 6.4 | -4.7 | 2.209 |
| 9 | 0.1 | 0.9 | 8.1 | -4.6 | 2.116 |
| 10 | 0.1 | 1 | 10 | -4.5 | 2.025 |
| 𝚺 | 1 | 5.5 | 38.5 | -49.5 | 24.585 |
| 𝚺² | 1 | 30.25 | 1482.25 | 2450.25 | 604.422225 |
| 𝚿 | 0.1 | 0.55 | 3.85 | -4.95 | 2.4585 |
| 𝚫 | 0 | 0.9 | 9.9 | 0.9 | 0.891 |
| 𝚸² | 0.1 | 3.85 | 253.33 | 245.85 | 61.251333 |
| 𝚸 | 0.316227766 | 1.962141687 | 15.9163438 | 15.67960459 | 7.826323083 |
| Calculada | Teórica | ||||
| ESPERANZA | 5.5 | 5.5 | |||
| VARIANZA | 8.25 | 8.25 |
Legend:
- 🔰 Start here
- 🏵️ Important Insight
- ⚙️ Technical and applications
- 💻 Some Code
- ⚜️ Advanced Topics
- 00.1 What Is Data?
-
00.2 Three Models of Reality (planned)
- 00.2.1 The Linguistic
- 00.2.2 The Symbolic
- 00.3.3 The Mathematical
- 00.3 Maps and Reality
- 00.4 Aside: Hossack, data as structure
- 00.5 Aside: Chomsky, data as evidence
- 01.1 🔰 Sets and Logic (planned)
- 01.2 Sample Space
- 01.3 Randomness and Pattern
- 01.4 Outcomes and Events
- 01.5 Finite and Infinite Sample Spaces
- 02.1 🔰 Functions (planned)
- 02.2 Types Are Alphabets
- 02.3 Random Variables: X : Ω → 𝒳 (planned)
- 02.4 Measurement Scales
- 02.5 Numerics
- 03.1 🔰 Fractions and Proportions (planned)
- 03.2 Randomness vs Uncertainty
- 03.3 Probability: Events, Conditioning, Bayes
- 03.4 Bayes' Theorem, Step by Step
- 03.5 Expected Value and Variance (planned)
- 03.6 Bernoulli (ES)
- 03.7 Discrete Uniform (ES)
- 03.8 🧗 Kolmogorov: Ω, 𝒜, ℙ
- 04.1 🔰 Powers and Logarithms (planned)
- 04.2 Rarity
- 04.3 Information Theory (Shannon)
- 04.4 Entropy: Three Views
- 05.1 🔰 Sums and Averages (planned)
- 05.2 Data, Measured
- 05.3 Sampling (ES)
- 05.4 Population and Sample (ES)
- 05.5 Types of Sampling (ES)
- 05.6 Choosing Summaries & Visuals
- 05.7 Visualization
- 05.8 Plots
- 06.1 What Is Statistics?
- 06.2 🔰 Curves and Area (planned)
- 06.3 Statistical Thinking
- 06.4 Variation
- 06.5 Uncertainty
- 06.6 Parameters vs Estimators (ES)
- 06.7 Distributions
- 06.8 Statistical Modeling
- 06.9 Statistical Methods
- 07.1 Data Storage: Databases (stub)
- 07.2 ER Diagrams
- 07.3 Chen Diagrams
- 07.4 SQL (planned)
- 07.5 Formal Languages and Regular Expressions (planned)
- 07.6 Reproducibility (planned)
08 · μ(x) — Fuzzy - When data is flexible
- 08.1 Fuzzy Logic (planned)
- 08.2 Fuzzy Numbers (planned)
- 08.3 Possibility vs Probability (planned)
09 · x(t) — Signals and systems - Where patterns live
- 09.1 Signals (stub)
- 09.2 Sampling and Aliasing (planned)
- 09.3 Convolution (planned)
- 09.4 Frequency (planned)
- 10.1 🔰 Vectors and Matrices (planned)
- 10.2 Mathematical Modelling (planned)
- 11.1 🔰 Derivatives (planned)
- 11.2 Convex Optimisation (planned)
- 12.1 Supervised Learning (planned)
- 12.2 Data in Language Models
- 13.1 Cause and Effect (planned)
- 13.2 Capstone Guide (planned)