Before talking about the full-fledged data analysis process and diving into the details of individual methods, this chapter demonstrates some typical pitfalls one encounters when analyzing real-world data. We start our journey through the data analysis process by looking over the shoulders of two (...
Analysis methods for deformation detection using TLS and UAS data on the example of a landslide simulationTechnical Note Open access Published: 14 March 2024 Volume 15, article number 9, (2024) Cite this article Download PDF You have full access to this open access article ...
Aside from the usefulness mentioned above, the key difference between these two types of tracking errors lie in their calculation methods. Ex-post uses actual historical returns, while ex-ante uses predictive models and current portfolio characteristics. For this reason, it may be easier to get ex...
Hypothesis testing relies exclusively on data and doesn’t provide a comprehensive understanding of the subject being studied. Additionally, the accuracy of the results depends on the quality of the available data and the statistical methods used. Inaccurate data or inappropriate hypothesis formulation ma...
To identify patterns and trends, quantitative research methods make use of numbers. These methods are used for hypothesis testing, action impact measurement, and data-driven prediction. Key quantitative research methods include: Surveys: Surveys are employed to collect data from many people. These su...
Also, in-sample fit metrics, such as information criteria (for example, AIC or BIC, see aicbic or Information Criteria for Model Selection), and out-of-sample forecast performance (as emphasized in Smets and Wouters [13]) are data-driven methods for model specification. In practice, ...
Geostatistical analysis of data occurs in the following phases: Modeling the semivariogram or covariance to analyze surface properties Kriging A number of kriging methods are available for surface creation in Geostatistical Analyst, including ordinary, simple, universal, indicator, probability, ...
The crossval and kfoldLoss methods can estimate the misclassification error for both LDA and QDA using the given data partition cp. Estimate the true test error for LDA using 10-fold stratified cross-validation. Get cvlda = crossval(lda,'CVPartition',cp); ldaCVErr = kfoldLoss(cvlda) ldaC...
By understanding the different needs and wants of individual segments, businesses can create targeted advertising and promotions that resonate with those consumers. This can save time and money, and minimize waste by avoiding mass marketing methods that may not be effective for certain groups. Informs...
Cluster analysis is one of the important data analysis methods in data mining research. "The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. A cluster is a collection of data objects that are similar to one another and are dissimi...