Summary Statistics
Despite advances in methodology, most scientific discoveries start with observing trends in data. To understand the dataset available to your organization you need a descriptive presentation of the basics. Summary statistics are the first step toward a more sophisticated statistical analysis. Intuitive tables provide non-technical readers with descriptive information about the composition of your dataset. At the same time, frequent summary statistics analysis can provide a lens to evaluate change over time as well as the dataset’s readiness for analysis. As experienced data scientists, we offer insights into data quality, data curation strategies, and salient statistical trends.
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Econometric Modeling
Effective solutions to complex challenges, require a robust understanding of direct and indirect associations among variables of interest. Validating of the way data behaviors are connected (or not connected) is useful developing a more nuanced understanding of the world. It also instrumental to identifying solutions and setting intervention goals. Our group of experienced social scientists possesses the research expertise required to provide reliable estimates of causal relationships. Aletheia is comprised of individuals, whose publications and professional experience reflect a commitment to unbiased research. Our team is capable of developing innovative approaches to practical research.
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Factor Analysis
Factor analysis is a powerful tool for identifying latent variables and analyzing structural relationships. Comprehensive factor analysis estimates the number and contribution of different drivers emerging in the data. When there is more than one contributing force in a relationships between variables, it is helpful to understand relative predicted effects of individual forces. It is also possible to use the technique to reduce a large number of variables in a dataset to a fewer number of factors (or unobserved forces). Additionally, factor analysis has applications for survey design, questionnaire validation, consumer behavior, organizational behavior and cluster analysis.
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Cluster Analysis
Making sense of chaotic systems requires innovative approaches to attribute analysis. By uncovering patterns and modeling groups, hidden patterns can be revealed in large datasets. Cluster analysis can be used to harvest the intrinsic power of existing data. What genes are responsible for specific diseases? How many customer segments exist in a specific market? What countries are most likely to enter into a new trade agreement? Applications of cluster analysis are transferable to many industries and policy areas.
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Forecasts
Our team of talented researchers possess the tools and experience to predict trajectories of quantities of interest under different scenarios. Our predictive analytics evaluate various futures by simulating the factors that can shape their attributes. Estimates are most useful for scenario planning and risk mitigation in cases where causality is of less importance than the accuracy of predictions. A realistic range of probable outcomes is helpful in preparing for circumstances that may arrive as a result of changing institutions or market conditions.
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