476998 Estadística → sin propuesta
El programa docente de Estadística (origen) no tiene sección de Resultados de Aprendizaje/Competencias. En cambio, se da un reasoning diciendo que "no assoleix competències clau com l'ús de programari estadístic o el teorema del límit central" de l'assignatura en destí que més és similar (Estadística UOC 05.568). La IA debería decirnos claramente que la asignatura origen no tiene tal sección, y que con los contenidos que aparecen, tampoco llega a revisar ni a aceptar...
El programa de origen no tiene sección de resultados de aprendizaje (la extracción produjo 0 LOs y 12 contenidos). Sin LOs y con idioma distinto, el techo alcanzable es 80. En el run, la cobertura de contenidos fue 5/8 → matching 50 × confianza 92 = 46. La tabla muestra el recálculo: los tres bloques de 05.568 que quedan sin cubrir o en zona dudosa (descriptiva, teorema del límite central, contraste de dos muestras) son exactamente los que el motor citó como carencias.
La petición del cliente es legítima: el motor no avisa de que el origen carece de sección de LOs — trata la ausencia como cobertura cero y el redactado lo presenta como «mancances». Mejora recomendada: flag explícito en la salida.
| Ítem de la asignatura UOC | Estado | Coseno | Ítem del origen más próximo |
|---|---|---|---|
| CT Descriptive Statistics. Introduction to data analysis. | Franja juez · rechazada en el run | 0,823 | Multivariable probability. Measures of relationship between variables. Mean. Variance. Covariance. Correlation. Linear regression |
| Juez (reproducción · RECHAZADA): The UOC item focuses on 'Descriptive Statistics' and an 'Introduction to data analysis'. All the candidate items from the external syllabus cover topics related to Probability Theory (random variables, probability functions, Bayes' theorem, multivariable probability) and Statistical Inference (sampling, point estimation, confidence intervals). None of the candidates explicitly address descriptive statistics (such as data visualization, frequency distributions, or exploratory data analysis techniques) at an equivalent level of specificity, as they are oriented towards probability and inferential statistics. | |||
| CT Probability and random variables. | Cubierta (auto) | 0,919 | Random variables. Functions. Probability function. Density function. Distribution function |
| CT Statistical Inference. | Cubierta (auto) | 0,909 | Statistical inference. Fundamentals. Statistical sampling. Sample values and population values. Point estimation |
| CT Central limit theorem. | Franja juez · rechazada en el run | 0,823 | Probability. Concept. Properties. Conditional probability. Dependence and independence. Bayes' theorem |
| Juez (reproducción · RECHAZADA): The UOC item specifically targets the 'Central limit theorem' (Teorema del límit central). None of the candidate items from the external syllabus explicitly mention or cover this fundamental theorem of probability and statistics. While some candidates cover related foundational topics such as probability, random variables, continuous distributions (Normal, Student's t), and statistical inference, they do not explicitly address the Central Limit Theorem itself, which is a specific and crucial mathematical result linking sample means to the normal distribution. | |||
| CT Confidence intervals. | Cubierta (auto) | 0,904 | Statistical inference. Intervals. Characteristic intervals. Characteristic interval for the mean |
| CT Hypothesis Testing. | Cubierta (auto) | 0,875 | Validity of inference tests. Two-tailed hypothesis test. One-tailed hypothesis test. Type I and Type II errors |
| CT Testing of two samples. | Franja juez · rechazada en el run | 0,827 | Validity of inference tests. Two-tailed hypothesis test. One-tailed hypothesis test. Type I and Type II errors |
| Juez (reproducción · RECHAZADA): The UOC item specifically targets the 'Testing of two samples' (comparisons between two groups/samples, such as two-sample t-tests or A/B testing). Candidate 0 covers general hypothesis testing concepts (validity, one/two-tailed tests, Type I/II errors) but does not explicitly address the specific scenario of two-sample testing. Candidate 1 covers general statistical inference, sampling, and point estimation, which is also too generic and does not cover two-sample tests. | |||
| CT Linear Regression. | Cubierta (auto) | 0,844 | Multivariable probability. Measures of relationship between variables. Mean. Variance. Covariance. Correlation. Linear regression |
| LO Familiarize with the types of problems solved using statistical methods. | No cubierta | — | — |
| LO Study data description and interpretation of results. | No cubierta | — | — |
| LO Use statistical software to perform calculations. | No cubierta | — | — |
| LO Understand the concept of random phenomena and their modeling through probability and random variables. | No cubierta | — | — |
| LO Identify the most common probability distributions, with a special focus on the normal distribution. | No cubierta | — | — |
| LO Get introduced to Student's t-distributions. | No cubierta | — | — |
| LO Describe the special characteristics of the arithmetic mean of a dataset through the Central Limit Theorem. | No cubierta | — | — |
| LO Use confidence intervals to estimate means and proportions. | No cubierta | — | — |
| LO Get introduced to the concept of quality control and its applications. | No cubierta | — | — |
| LO Learn to test hypotheses and understand the concepts of null and alternative hypotheses, Type I and II errors, significance level, test statistic, critical value, and p-value. | No cubierta | — | — |
| LO Know how to test hypotheses about the mean and proportion of a population. | No cubierta | — | — |
| LO Know how to test hypotheses about the difference between means and between proportions of two populations. | No cubierta | — | — |
| LO Understand the simple linear regression model and perform inference on the slope of the regression line. | No cubierta | — | — |
| LO Study the goodness of fit using the coefficient of determination. | No cubierta | — | — |