The factor analysis method is an analysis method developed on the basis of principal component analysis, and its main target of study is the degree of connection within the matrix, that is, taking the matrix with the original index data as the basis, studying the internal structure of this matrix, and then searching for independent new factors that have a dominant effect on this structure so as to locate those particular factors that can influence the variables. The purpose of factor analysis is not to find the main factors [19, 20] but to know what these factors stand for. But the principal component analysis method finds the initial loading matrix of the solution of the principal factor that does not satisfy the simple structure principle and the typical variables represented by each factor are not very prominent, thus leading to ambiguity in the meaning of the factors. Therefore, it is not easy to explain economically by factors. For this, it is possible to rotate the factors to obtain satisfactory results [21].
mathematical statistics and data analysis solutions manual pdf 12
The basic interpretation of cluster analysis is to summarize variables with similar properties by counting the distribution of variables and summarizing them in the analysis process so as to achieve a statistical approach for the purpose of reducing systematic variables. Actually, the cluster analysis method is a way to find a statistic, that is, a statistic that can objectively reflect the degree of close association between variables and classify these variables on that basis [22]. The two commonly used clustering statistics are coefficient of distance and similarity coefficient. However, there are three kinds of cluster analysis methods: systematic clustering method, tuning method, and graph theory method.
This book introduces core elements of causal inference into undergraduate and lower-division graduate classes in statistics and data-intensive sciences. The aim is to provide students with the understanding of how data are generated and interpreted at the earliest stage of their statistics education. To that end, the book empowers students with models and tools that answer nontrivial causal questions using vivid examples and simple mathematics. Topics include: causal models, model testing, effects of interventions, mediation and counterfactuals, in both linear and nonparametric systems.
Recently collected retrospective data (from 2000-2001) on Italian university students are analyzed to find out the most significant factors that accelerate or delay the entrance into a first couple relationship for teenagers. Intensity regression analysis is used to test factors that either proved to be noteworthy from previous analyses or are supposed to be significant from a theoretical point of view. Unobserved heterogeneity is included in the model to take into account the characteristics of individuals that are not measured or that are not measurable. The following results arise: age is highly significant, with a decreasing hazard after age 19. The influence of family, a strong institution in Italy, is noticeable. Poor communication with parents is negatively associated with entrance into the first romantic relationship while tolerance of a son's behaviors is positively associated. The social life of a young person also shapes this event: shyer adolescents had a lower relative risk compared to their contemporaries who had many leisure interests and a wider friendship network. As expected, lower satisfaction with self-appearance exerts a negative weight on the hazard. Finally, unobserved heterogeneity is not found to be significant in the model.
Based on an analysis of a nationally representative rural household survey and various sources of aggregate statistics, we explore patterns of residential energy use in rural China within the conceptual framework of the energy transition. We find that residential energy consumption varies tremendously across geographic regions due to disparities of access to different energy sources, prices, climate, income, and urbanization level. Household demographic characteristics, in particular household size, have important impacts on residential energy use. Aggregate time series data show that the transition from biomass to modern commercial sources is still at an early stage, and cross-sectional data suggest that incomes may have to rise substantially in order for absolute biomass use to fall. We also find that energy use patterns as a function of net income, rather than total expenditure, are more consistent with the energy transition model in rural China. 2ff7e9595c
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