Bloque 6: Interdependencia y ACP

<p>Una matriz de correlaciones. (B)</p> Signup and view all the answers

<p>Una técnica para reducir la dimensión de múltiples variables. (D)</p> Signup and view all the answers

<p>Escalas de intervalo o de razón. (A)</p> Signup and view all the answers

<p>Componentes latentes. (A)</p> Signup and view all the answers

<p>Es crucial, afecta directamente la solución hallada. (D)</p> Signup and view all the answers

<p>El espacio muestral donde se representan los individuos mediante variables originales. (D)</p> Signup and view all the answers

<p>Esperanza de vida femenina (C)</p> Signup and view all the answers

<p>Tienen una correlación positiva fuerte (B)</p> Signup and view all the answers

<p>Correlación negativa fuerte (C)</p> Signup and view all the answers

<p>Alfabetización (%) (D)</p> Signup and view all the answers

<p>Habitantes en ciudades (%) (A)</p> Signup and view all the answers

<p>A mayor alfabetización, menor mortalidad infantil (C)</p> Signup and view all the answers

<p>Correlación negativa fuerte (B)</p> Signup and view all the answers

<p>Correlación negativa fuerte (B)</p> Signup and view all the answers

<p>El estudio de poblaciones y sus características. (D)</p> Signup and view all the answers

<p>Se pueden usar variables inadecuadas para medir la realidad. (B)</p> Signup and view all the answers

<p>La complejidad es independiente de la cantidad de variables. (B)</p> Signup and view all the answers

<p>Hay un solapamiento significativo en la información que comparten. (A)</p> Signup and view all the answers

<p>Identificar la estructura subyacente de matrices de información redundante. (A)</p> Signup and view all the answers

<p>Pueden estar relacionados pero no necesariamente miden lo mismo. (D)</p> Signup and view all the answers

<p>Puede resultar en una sobrecarga de información sin mejorar la comprensión. (A)</p> Signup and view all the answers

<p>Puede ser útil pero a veces se asocia con confusión o variabilidad. (C)</p> Signup and view all the answers

<p>Reducir la dimensión del conjunto de datos (C)</p> Signup and view all the answers

<p>Son numéricas y escaladas (C)</p> Signup and view all the answers

<p>Las variables explican el mismo fenómeno (C)</p> Signup and view all the answers

<p>Nuevas variables no correlacionadas (C)</p> Signup and view all the answers

<p>Porque reduce el riesgo de colinealidad entre variables (C)</p> Signup and view all the answers

<p>Una parte de la información original (C)</p> Signup and view all the answers

<p>Técnica de interdependencia (C)</p> Signup and view all the answers

<p>Varianza (C)</p> Signup and view all the answers

A method to explore relationships between multiple variables.

Strong relationship between two variables. Values tend to change together.

A method to reduce the dimensionality of data by combining correlated variables into fewer new variables (principal components).

Selecting the number of principal components that adequately explain the variance in the original data.

Process of transforming principal components into a way that's more interpretable.

A negative correlation exists between the level of literacy and infant mortality rate. In simple terms, more literate populations tend to have lower infant mortality rates.

A strong negative correlation exists between infant mortality and female life expectancy. Lower infant mortality rates are linked to longer lives for women.

A strong positive correlation exists between the average number of children per family and the fertility rate; thus families with more children tend to have higher birth rates.

A negative correlation exists between the log of GDP per capita and infant mortality; indicating wealthier countries tend to have fewer infant deaths.

A negative correlation is present between the percentage of urban population and infant mortality rates suggesting an inverse relationship between urbanization and mortality.

A negative correlation is observable between the fertility rate and the log of GDP per capita; higher economic development is often associated with lower birth rates.

A table presenting correlations between various demographic and economic factors, providing insight into relationships between them.

A numerical value (between -1 and +1) that quantifies the strength and direction of a relationship between two variables.

A demographic phenomenon is a measurable social trend or event, such as population growth or migration patterns.

Determining the magnitude or characteristics of demographic phenomena involves using various indicators.

Precise, measurable descriptions that allow for the accurate evaluation of a concept, particularly in social sciences.

Abstract ideas, like Economic Development or Customer Satisfaction, studied in the social sciences.

A statistical relationship between variables. If one changes, the other may also change in a predictable way.

Multiple indicators that convey much of the same information.

Statistical techniques aimed at understanding the hidden structure within data with overlapping metrics.

The apparent complexity of a phenomenon does not always reflect its true complexity. More metrics do not necessarily mean a phenomenon is more complicated

Principal Component Analysis (PCA) reduces data dimensionality by creating new variables (components) from combinations of original variables.

New variables created by linear combinations of original variables in Principal Component Analysis(PCA); latent variables that are not directly observable.

Principal Component Analysis (PCA) requires variables measured on interval or ratio scales (numerical values).

The space defined by the original variables in principal component analysis , where individuals are plotted.

Principal Component Analysis creates a reduced-dimension space, condensing data while retaining essential information from original variables to use in subsequent analysis.

A statistical method to reduce the number of variables in a dataset by combining related variables into new ones called principal components.

The process of reducing the number of variables in a dataset while keeping relevant information.

Selection of the number of principal components that retains a significant amount of variance from the original data.

Variables that contain similar information. High correlation between many variables.

Strong relationship between variables. Variables tend to change together.

Measured by variance in the data. Represents the amount and kind of information carried by the variable.

Variables in a dataset that contain similar information and are highly correlated

Statistical method for classifying observations, based on similar patterns in variables studied