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Title:           Métodos Cuantitativos para la Investigación de Mercado y el Marketing
Author:          Giuseppe Lamberti
Creator:         LaTeX via pandoc
Producer:        xdvipdfmx (20250410)
CreationDate:    Mon Dec 15 15:57:31 2025 CET
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Pages:           250
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    PageMode: UseOutlines
    Outlines: 
     Item: 
      Title: Estadística descriptiva
     Item: 
      Title: 1. Introducción
     Item: 
      Title: 2. ¿A que sirve?
     Item: 
      Title: 3. Diferencia entre las variables numericas y categoricas.
     Item: 
      Title: 4. Estadistica descriptiva univariante
      Children: 
       Item: 
        Title: 4.1 Principales gráficos y medidas para variables numericas (analisis univariante)
       Item: 
        Title: 4.2 Principales gráficos y medidas para variables categóricas
     Item: 
      Title: 5. Estadistica descriptiva bivariante
      Children: 
       Item: 
        Title: 5.1 Principales gráficos y medidas para variables numericas (analisis bivariante)
       Item: 
        Title: 5.2 Principales gráficos y medidas para variables categoricas (analisis bivariante)
       Item: 
        Title: 5.2.1 Interpretación como Probabilidades
       Item: 
        Title: 5.3 Principales gráficos para una variable numerica y unacategorica (analisis bivariante)
     Item: 
      Title: 6. Software estadísticos R, STATA, y JMP (SAS)
      Children: 
       Item: 
        Title: 6.1. R
        Children: 
         Item: 
          Title: 6.1.1 Estadísticas univariantes en R
         Item: 
          Title: 6.1.2 Estadísticas bivariantes en R
       Item: 
        Title: 6.2. Stata
        Children: 
         Item: 
          Title: 6.2.1. Estadísticas univariantes en Stata
         Item: 
          Title: 6.2.2. Estadísticas bivariantes en Stata
       Item: 
        Title: 6.3. Estadísticas Univariantes y Bivariantes en JMP
     Item: 
      Title: Estadística inferencial
     Item: 
      Title: 1. Introducción a la Inferencia Estadística y Muestras
     Item: 
      Title: 2. Límites para trabajar con datos poblacionales
     Item: 
      Title: 3. Cambios en la Interpretación
      Children: 
       Item: 
        Title: 3.1. ¿Por qué existe incertidumbre en los datos muestrales?
       Item: 
        Title: 3.2. ¿Cómo manejamos esta incertidumbre?
     Item: 
      Title: 4. Intervalos de Confianza
      Children: 
       Item: 
        Title: 4.1. Factores que Afectan la Amplitud del IC
       Item: 
        Title: 4.2. Aplicón de los intervalos de confianza en la investigación de mercado
     Item: 
      Title: 5. Test Estadísticos
      Children: 
       Item: 
        Title: 5.1. ¿Qué son las Hipótesis y los Estadísticos?
       Item: 
        Title: 5.2. Los Errores en los Test Estadistico
       Item: 
        Title: 5.3. Los Principales Test Estadisticos Usados en Investigación de Mercado
       Item: 
        Title: 5.3.1. Test t: Comparación de medias
       Item: 
        Title: 5.3.2. ANOVA
       Item: 
        Title: 5.3.2. Test Chi-cuadrado (X²)
     Item: 
      Title: 6. Tecnicas de Muestreo
      Children: 
       Item: 
        Title: 6.1. Muestreo Probabilístico
        Children: 
         Item: 
          Title: 6.1.1. Muestreo aleatorio simple
         Item: 
          Title: 6.1.2. Muestreo sistemático
         Item: 
          Title: 6.1.3. Muestreo por conglomerados (cluster)
       Item: 
        Title: 6.2. Muestreo No Probabilístico
        Children: 
         Item: 
          Title: 6.2.1. Muestreo de conveniencia
         Item: 
          Title: 6.2.2. Muestreo por respuesta voluntaria
         Item: 
          Title: 6.2.3. Muestreo en bola de nieve (snowball)
         Item: 
          Title: 6.2.4. Muestreo por juicio o propósito (purposive)
       Item: 
        Title: 6.3. Muestreo Estratificado o por cuotas
     Item: 
      Title: 7. Tamaño Muestral
      Children: 
       Item: 
        Title: 7.1.Parámetros para determinar el tamaño de la muestra
       Item: 
        Title: 7.2. Fórmulas para calcular el tamaño de la muestra
       Item: 
        Title: 7.3. Ejemplo: Cálculo del tamaño de la muestra
       Item: 
        Title: 7.4. Tabla: Tamaños de muestra para diferentes márgenes de error y niveles de confianza
       Item: 
        Title: Tabla de Tamaños de Muestra
     Item: 
      Title: Regresión múltiple
     Item: 
      Title: 1. Introducción
     Item: 
      Title: 2. Regresión Lineal Múltiple: Definición y Fórmula
     Item: 
      Title: 2. Hipótesis del Modelo
     Item: 
      Title: 3. Estimación de los parametros del modelo (Metodo de los minimos cuadrado OLS)
     Item: 
      Title: 4. Significancia de los Parámetros
     Item: 
      Title: 5. Validación del Modelo
     Item: 
      Title: 6. Validación de los Residuos en la Regresión Lineal Múltiple
      Children: 
       Item: 
        Title: 6.1. Residuos
       Item: 
        Title: 6.1. Gráficos de Validación de los Residuos
       Item: 
        Title: 6.2. Pruebas Estadísticas para los Residuos
     Item: 
      Title: 7. Multicolinealidad en la Regresión Lineal Múltiple
      Children: 
       Item: 
        Title: 7.1. Como se soluciona
     Item: 
      Title: 8. Ejemplos
      Children: 
       Item: 
        Title: 8.1. Caso de Estudio: Presupuesto publicitario
       Item: 
        Title: 8.2. Caso de Estudio: Producto Interno Bruto
       Item: 
        Title: 8.3. Caso de Estudio: U invertida
     Item: 
      Title: 9. Software estadísticos R, STATA, y JMP (SAS)
      Children: 
       Item: 
        Title: 9.1. Regresión Lineal Múltiple en R
       Item: 
        Title: 9.2. Regresión Lineal Múltiple en STATA
     Item: 
      Title: 9.3. Regresión Lineal Múltiple en JMP (SAS)
     Item: 
      Title: ANEXO 1. Estimar una regression manualmente.
     Item: 
      Title: Regresión simple
     Item: 
      Title: 1. Introducción
      Children: 
       Item: 
        Title: 1.1. ¿Qué es la regresión lineal?
       Item: 
        Title: 1.2. ¿Para qué sirve la regresión lineal?
       Item: 
        Title: 1.3. Aplicaciones de la regresión lineal
     Item: 
      Title: 2. Hipótesis del modelo de regresión lineal simple
     Item: 
      Title: 3. Como se estiman los parametros del modelo (metodo de los minimos cuadrado OLS)
      Children: 
       Item: 
        Title: 3.1. Cálculo de los parámetros mediante mínimos cuadrados
        Children: 
         Item: 
          Title: Resumen
       Item: 
        Title: 3.2. Ejemplo práctico
     Item: 
      Title: 4. Significancia de los parámetros
     Item: 
      Title: 5. Validación del Modelo
     Item: 
      Title: 6. Validación de los Residuos en un Modelo de Regresión Lineal Simple
      Children: 
       Item: 
        Title: 6.1. Gráficos
       Item: 
        Title: 6.2. Pruebas Estadísticas para Validar Residuos
     Item: 
      Title: 7. Ejemplo: Analisis de la relación entre el ingreso personal y el nivel de consumo
     Item: 
      Title: 8. Software estadísticos R, STATA, y JMP (SAS)
      Children: 
       Item: 
        Title: 8.1. Regresión Lineal en R
        Children: 
         Item: 
          Title: Ejemplo en R
       Item: 
        Title: 8.2. Regresión Lineal en STATA
        Children: 
         Item: 
          Title: Ejemplo en STATA
       Item: 
        Title: 8.3. Regresión Lineal en JMP (SAS)
        Children: 
         Item: 
          Title: Realizar la regresión en JMP
         Item: 
          Title: Parámetros y Salidas principales en JMP
         Item: 
          Title: Ejemplo en JMP
     Item: 
      Title: Regresión logística
     Item: 
      Title: 1. Introducción
     Item: 
      Title: 2. ¿Por qué usar regresión logística en lugar de regresión lineal para variables categóricas?
      Children: 
       Item: 
        Title: 2.1. Demostración
     Item: 
      Title: 3. Concepto de ODDS, razón de probabilidad (ODDS ratio) y logaritmo de ODDS
     Item: 
      Title: 4. Estimación de los parámetros: el método de máxima verosimilitud
     Item: 
      Title: 5. Evaluación del modelo
     Item: 
      Title: 6. Interpretación del modelo
      Children: 
       Item: 
        Title: 6.1. Algunas reglas básicas
        Children: 
         Item: 
          Title: Interpretación de los odds ratio
       Item: 
        Title: 6.2. Predicciones
       Item: 
        Title: 6.3. Comparación de clasificación predicha y observaciones
        Children: 
         Item: 
          Title: Cálculo de métricas de evaluación
       Item: 
        Title: 7. La regresión logística múltiple
     Item: 
      Title: Ejemplos
      Children: 
       Item: 
        Title: 8.1 Vinoteca
        Children: 
         Item: 
          Title: 8.1.1. Análisis descriptiva
         Item: 
          Title: 8.1.2. Estimación modelo Purchased ~ Price
         Item: 
          Title: 8.1.3. Estimación modelo Purchased ~ Price+Quality
         Item: 
          Title: 8.1.4. Evaluación del modelo
         Item: 
          Title: 8.1.5. Matriz de confusión
         Item: 
          Title: Cálculo de métricas: Accuracy y Error
         Item: 
          Title: Cálculo de pérdidas en la campaña
       Item: 
        Title: 8.2 Caso de estudio: Campaña de préstamos juveniles
        Children: 
         Item: 
          Title: Evaluación previa a la nueva campaña
         Item: 
          Title: 8.2.1. Analisis descriptiva
         Item: 
          Title: 8.2.2. Estimación modelo impago ~ saldo
         Item: 
          Title: 8.2.3. Estimación modelo impago ~ saldo+ingresos+estudiante
         Item: 
          Title: 8.2.4.Conclusiones
     Item: 
      Title: Anexo Demostración Teórica: Verosimilitud en Regresión Logística''
      Children: 
       Item: 
        Title: A1. Paso 1: Definir la función de verosimilitud
       Item: 
        Title: A2. Paso 2: Tomar el logaritmo de la función de verosimilitud
       Item: 
        Title: A3. Paso 3: Derivar la función log-verosimilitud
       Item: 
        Title: A4. Paso 4: Resolver las ecuaciones para obtener los coeficientes
       Item: 
        Title: A5. Ejemplo calculado a mano
     Item: 
      Title: Análisis cluster
     Item: 
      Title: 1. Introducción al Clustering
     Item: 
      Title: 2. El Concepto de Distancias
      Children: 
       Item: 
        Title: 2.1. Distancia Euclidiana
       Item: 
        Title: 2.2. Otras Distancias
        Children: 
         Item: 
          Title: 2.2.1. Distancia de Manhattan
         Item: 
          Title: 2.2.2. Distancia Basada en Correlación
         Item: 
          Title: 2.2.3. Distancias para Variables Binarias
       Item: 
        Title: 2.3. El problema de la corrección entre variables.
     Item: 
      Title: 3. K-means
      Children: 
       Item: 
        Title: 3.1. Ventajas y Desventajas
       Item: 
        Title: 3.2. La funcion 
        Children: 
         Item: 
          Title: 3.2.1 Resultados Esperados
         Item: 
          Title: 3.2.2 Uso de la Función kmeans() en K-means Clustering
         Item: 
          Title: 3.2.3 Evaluación del Clustering
     Item: 
      Title: 4. Clúster jerárquico
      Children: 
       Item: 
        Title: 4.1. El Algoritmo de Agglomerative Hierarchical Clustering
        Children: 
         Item: 
          Title: 4.1.1. Tipos de Linkage
       Item: 
        Title: 4.2. El Dendrograma
       Item: 
        Title: 4.3. La Función HCPC en Clustering Jerárquico
        Children: 
         Item: 
          Title: 4.3.1. Descripción de los Grupos
         Item: 
          Title: 4.3.2. Individuos Representativos de los Clusters
     Item: 
      Title: 5. Ejemplos
      Children: 
       Item: 
        Title: 5.1. Nissan case
        Children: 
         Item: 
          Title: 5.1.2 Cluster jerárquico
         Item: 
          Title: 5.1.3 K-menas
       Item: 
        Title: 5.2. Securitas case
       Item: 
        Title: 5.2.1. Cluster jerárquico
       Item: 
        Title: 5.2.3 K-menas
     Item: 
      Title: Anexo A. Heatmaps
     Item: 
      Title: Anexo A. Dimostraciones clúster jerárquico y kmeans
      Children: 
       Item: 
        Title: Clúster Jerárquico
       Item: 
        Title: K-means
     Item: 
      Title: Componentes principales
     Item: 
      Title: 1. Introducción
     Item: 
      Title: 2. Presentación intuitiva
     Item: 
      Title: 3. Aplicaciones en Marketing
     Item: 
      Title: 4. El método
      Children: 
       Item: 
        Title: 4.1. Calculo de las componentes: interpretación geométrica
       Item: 
        Title: 4.2. Cálculo de los componentes: método matemático
       Item: 
        Title: 4.3. Reproducibilidad de los componentes
       Item: 
        Title: 4.4. Proporción de varianza explicada
       Item: 
        Title: 4.5. Número óptimo de componentes principales
       Item: 
        Title: 4.6. Interpretación de las Componentes
        Children: 
         Item: 
          Title: 4.6.1. Círculo de Correlaciones
         Item: 
          Title: 4.6.2. Correlación entre Componentes y Variables Originales
       Item: 
        Title: 4.7. Representación gráfica de los individuos e interpretación
       Item: 
        Title: 4.8 Índices de calidad de la representación
       Item: 
        Title: 4.9. La importancia de la matriz de correlaciones.
     Item: 
      Title: 5. Como realizar un ACP: pasos a seguir
      Children: 
       Item: 
        Title: Pasos a seguir:
       Item: 
        Title: Consideraciones:
     Item: 
      Title: 6. El mapa perceptual (biplot)
      Children: 
       Item: 
        Title: Ventajas del Mapa Perceptual:
       Item: 
        Title: Relación con el Análisis en Componentes Principales (ACP):
     Item: 
      Title: 7. Aplicaciones
      Children: 
       Item: 
        Title: 7.1 Mapa perceptual de posicionamiento (brand rating Survey)
       Item: 
        Title: Análisis en Componentes Principales aplicado a la Percepción de Marcas
        Children: 
         Item: 
          Title: Contexto del Estudio
         Item: 
          Title: Aspectos a Evaluar
         Item: 
          Title: Objetivos del ACP
         Item: 
          Title: 7.1.2 Análisis descriptiva y manipulación
         Item: 
          Title: 7.1.2 ACP
         Item: 
          Title: 7.1.3 Mapa perceptual
         Item: 
          Title: 7.1.4 Mapa perceptual como construirlo
       Item: 
        Title: 7.2 Securitas: posicionamiento
        Children: 
         Item: 
          Title: 7.2.1 Data-set
         Item: 
          Title: 7.2.2 ACP Step 1: análisis de las correlaciones
         Item: 
          Title: 7.2.3 ACP Step 2: Aplico la metodologia
         Item: 
          Title: 7.2.4 ACP step 3: identifico el numero de Componentes
         Item: 
          Title: 7.2.5 ACP Step 4: realizo el calculo de la ACP con dos componentes las calculo y las interpreto
         Item: 
          Title: 7.2.6 ACP Step 5: realizo el gráfico de los individuos y lo interpreto
       Item: 
        Title: 7.3 My Global Company (Promociones)
       Item: 
        Title: Promociones y Estrategias de Marketing
        Children: 
         Item: 
          Title: 7.3.1 Data-set
         Item: 
          Title: 7.3.2 ACP step 1: análisis de las correlaciones
         Item: 
          Title: 7.3.3 ACP step 2: aplico la metodología
         Item: 
          Title: 7.3.4 ACP step 3: identifico el numero de Componentes
         Item: 
          Title: 7.3.5 ACP step 4: realizo el calculo de la ACP con dos componentes las calculo y las interpreto
         Item: 
          Title: 7.4.6 ACP step 5: realizo el gráficos de los individuos y lo interpreto
         Item: 
          Title: 7.5 ACP para reducir las dimensiones y estimar una regresión linear (Principal Components Regression).
         Item: 
          Title: 7.5.1 Caso de Satisfacción Estudiantil
         Item: 
          Title: Análisis
         Item: 
          Title: 7.5.2 Método clásico: Estimación por método de regresión lineal
         Item: 
          Title: 7.5.2 Metodo PCA
     Item: 
      Title: Anexo 1: Como Construir un Mapa Perceptual en R.
     Item: 
      Title: Anexo 2: Demostración Teórica del Cálculo de Autovalores y Autovectores en PCA
      Children: 
       Item: 
        Title: 1. Definición de Autovalores y Autovectores
       Item: 
        Title: 2. Reformulación del Problema
       Item: 
        Title: 3. Condición de No-Trivialidad
       Item: 
        Title: 4. Cálculo de la Ecuación Característica
       Item: 
        Title: 5. Resolución de la Ecuación Característica
       Item: 
        Title: 6. Cálculo de Autovectores
       Item: 
        Title: 7. Cálculo de Componentes Principales
     Item: 
      Title: Análisis de correspondencias
     Item: 
      Title: 1. Introducción
      Children: 
       Item: 
        Title: Aplicaciones en Marketing
     Item: 
      Title: 2. Presentación intuitiva
      Children: 
       Item: 
        Title: 2.1. Punto de Partida: Tabla de Contingencia
       Item: 
        Title: 2.2. Representación Gráfica
     Item: 
      Title: 3. Transformaciones de las frecuencias observadas: frecuencias relativas, perfiles columnas y filas
      Children: 
       Item: 
        Title: 3.1. Pasos de la Transformación
        Children: 
         Item: 
          Title: 3.1.1. Cálculo de la Tabla de Contingencia
         Item: 
          Title: 3.1.2. Cálculo de las Frecuencias Relativas
         Item: 
          Title: 3.1.3. Cálculo de los Perfiles de Filas y Columnas
       Item: 
        Title: 3.2. Aplicación de la Descomposición en Valores Singulares
     Item: 
      Title: 4. Test de dependencia entre las variables categóricas.
      Children: 
       Item: 
        Title: 4.1. Hipótesis del Test de X^2
       Item: 
        Title: 4.2. Cálculo del Estadístico X^2
       Item: 
        Title: 4.3. Criterio de Decisión
     Item: 
      Title: 5. Elegimos el numero de las componentes
      Children: 
       Item: 
        Title: 5.1. Variabilidad Explicada
       Item: 
        Title: 5.2. Interpretación de Componentes
     Item: 
      Title: 6. Interpretación del grafico de los individuos
      Children: 
       Item: 
        Title: 6.1. Proximidad entre categorías
       Item: 
        Title: 6.2. Posición respecto al origen
       Item: 
        Title: 6.3. Puntos extremos y contrapuestos
        Children: 
         Item: 
          Title: 6.4. Índices de \cos^2 y de contribución absoluta
     Item: 
      Title: 7. Análisis de las correspondencias múltiples
      Children: 
       Item: 
        Title: 7.1. Ejemplo
       Item: 
        Title: 7.2. Matriz Disyuntiva Completa (Matriz Z)
       Item: 
        Title: 7.3. Relación entre Variables y la Matriz de Burt
       Item: 
        Title: 7.4. Matriz de Burt (B = Z'Z)
       Item: 
        Title: 7.5 Análisis e Interpretación
        Children: 
         Item: 
          Title: 7.5.1. Pasos de Interpretación:
     Item: 
      Title: 8. Ejemplos
     Item: 
      Title: 8.1 Tareas Domesticas
      Children: 
       Item: 
        Title: Uso de balloonplot en R
       Item: 
        Title: Paso 1: Selección del Número de Componentes
       Item: 
        Title: Paso 2: Análisis de los Cosenos Cuadrados (\cos^2) y Contribuciones
       Item: 
        Title: Paso 3: Interpretación de los Resultados
       Item: 
        Title: Paso 4: Visualización del Análisis de Correspondencias
     Item: 
      Title: 8.2 Tipologías de productos VS tipos de clientes. Análisis de las preferencias de los consumidores
      Children: 
       Item: 
        Title: Objetivo del Caso de Estudio
       Item: 
        Title: 1. Estadística Descriptiva
       Item: 
        Title: Interpretación del Gráfico
       Item: 
        Title: 8.3 Encuesta Consumo Tea
        Children: 
         Item: 
          Title: MCA
         Item: 
          Title: Análisis Clúster basado en el ACM
     Item: 
      Title: Anexo 1: Como se calculan los perfiles lineas y columnas
      Children: 
       Item: 
        Title: 1. Tabla de Contingencia
       Item: 
        Title: 2. Cálculo de las Frecuencias Relativas
       Item: 
        Title: 3. Perfiles de Filas
       Item: 
        Title: 4. Perfiles de Columnas
       Item: 
        Title: 5. Ejemplo Numérico
        Children: 
         Item: 
          Title: Perfiles de Filas
         Item: 
          Title: Perfiles de Columnas
     Item: 
      Title: Anexo 2: Como se calculan los perfiles lineas y columnas
      Children: 
       Item: 
        Title: 1. Matriz Disyuntiva Completa
        Children: 
         Item: 
          Title: Ejemplo
       Item: 
        Title: 2. Matriz de Burt
        Children: 
         Item: 
          Title: Estructura de la Matriz de Burt
         Item: 
          Title: Ejemplo de Cálculo
         Item: 
          Title: Interpretación
     Item: 
      Title: Anexo 3 Calculo de las componentes en el analisis de las correspondencias:
      Children: 
       Item: 
        Title: 1. Tabla de Contingencia
       Item: 
        Title: 2. Frecuencias Relativas
       Item: 
        Title: 3. Matriz de Inercia
       Item: 
        Title: 4. Matriz de Residuos (Matriz de Desviación)
       Item: 
        Title: 5. Descomposición en Valores Singulares
       Item: 
        Title: 6. Cálculo de las Coordenadas
       Item: 
        Title: 7. Interpretación de las Componentes
        Children: 
         Item: 
          Title: Inercia Explicada
     Item: 
      Title: PLS-SEM
     Item: 
      Title: 1. Introducción
     Item: 
      Title: 2. Variables latentes
     Item: 
      Title: 3. ¿Cómo se construye una variable latente? Relaciones entre variables latentes y manifiestas.
     Item: 
      Title: 4. ¿Cómo se relacionan las variables latentes? Modelos de ecuaciones estructurales
      Children: 
       Item: 
        Title: 4.1. Modelo estructural
       Item: 
        Title: 4.2. Modelo de medida
       Item: 
        Title: 4.3. Ejemplo aplicado: Modelo ECSI
     Item: 
      Title: 5. Dos enfoques posibles
      Children: 
       Item: 
        Title: 5.1. SEM Clásico (Hard-Modeling)
       Item: 
        Title: 5.2. PLS-SEM (Soft-Modeling)
       Item: 
        Title: 5.3. Comparación entre SEM Clásico y PLS-SEM
       Item: 
        Title: 5.4. Ejemplo Aplicado
     Item: 
      Title: 6. PLS-PM el modelo
      Children: 
       Item: 
        Title: 6.1. Especificación del Modelo
        Children: 
         Item: 
          Title: 6.1.1. Modelo Estructural
         Item: 
          Title: 6.1.2. Modelo de Medidas
       Item: 
        Title: 6.2. Ejemplo Teórico
        Children: 
         Item: 
          Title: 6.2.1. Modelo estructural (relación entre variables latentes)
         Item: 
          Title: 6.2.2. Modelo de medidas (método reflexivo)
         Item: 
          Title: 6.2.3. Modelo de medidas (método formativo)
       Item: 
        Title: 7. Algoritmo PLS-PM
       Item: 
        Title: 7.1. Descripción Intuitiva
       Item: 
        Title: 7.2. Descripción Técnica
        Children: 
         Item: 
          Title: 7.2.1. Paso 1: Inicialización
         Item: 
          Title: 7.2.2. Paso 2: Estimación de los pesos
         Item: 
          Title: 7.2.3. Paso 3: Estimación de las variables latentes
         Item: 
          Title: 7.2.4. Paso 4: Estimación de los coeficientes estructurales
       Item: 
        Title: 7.3. Ejemplo Calculado a Mano
        Children: 
         Item: 
          Title: 7.3.1. Paso 1: Inicialización
         Item: 
          Title: 7.3.2. Paso 2: Estimación de los pesos
         Item: 
          Title: 7.3.3. Paso 3: Actualización de las variables latentes
         Item: 
          Title: 7.3.4. Paso 4: Estimación de los coeficientes estructurales
     Item: 
      Title: 8. Validación del Modelo PLS
      Children: 
       Item: 
        Title: 8.1. Validación del Modelo de Medidas
        Children: 
         Item: 
          Title: 8.1.1. Validación del Modelo de Medidas: Modalidad Reflexiva
         Item: 
          Title: 8.1.2.Validación del Modelo de Medidas: Modalidad Formativa
       Item: 
        Title: 8.2. Validación del Modelo Estructural
        Children: 
         Item: 
          Title: 8.2.1. Criterios Recientes para Evaluar el Modelo Estructural en PLS-PM
       Item: 
        Title: 8.3. Tabla de Criterios para Evaluar el Modelo Estructural en PLS-PM
     Item: 
      Title: 9. PLS-PM in R
      Children: 
       Item: 
        Title: 9.1. Instalación y carga de librerías
       Item: 
        Title: 9.2. Descripción del modelo
        Children: 
         Item: 
          Title: 9.2.1. Definición del modelo
       Item: 
        Title: 9.3. Estimación del modelo
       Item: 
        Title: 9.4. Resultados del modelo
        Children: 
         Item: 
          Title: 9.4.1. Cargas de los indicadores
         Item: 
          Title: 9.4.2. Coeficientes estructurales
         Item: 
          Title: 9.4.3. Índices de ajuste
       Item: 
        Title: 9.5. Ejemplo completo
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  Checksum: d1698deb
   Type: CRC32
  Checksum: e92ef3c6b5787da5a37947b4bf432a26
   Type: MD5
  Checksum: dba66cb026a297a2b7f20d0367dbc1da0e0c095f
   Type: SHA-1
  Checksum: c3f47d89cd904525db0cd7fe5e125c1176871caf48c55ddbbcca563e365a1082
   Type: SHA-256