Beginner's Guide to Computation in Statistical Research: Difference between revisions
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While the foundations of statistical methods are grounded in mathematical theory, we often need to demonstrate that these methods '''are useful in practice'''. This can done via toy examples, simulation studies, and applications to real-world datasets. We do not recommend spending hundreds of hours running calculations on datasets by hand. Thus, we will introduce the reader to core concepts of statistical computing and relevant resources in the CMU Department of Statistics and Data Science. | While the foundations of statistical methods are grounded in mathematical theory, we often need to demonstrate that these methods '''are useful in practice'''. This can done via toy examples, simulation studies, and applications to real-world datasets. We do not recommend spending hundreds of hours running calculations on datasets by hand. Thus, we will introduce the reader to core concepts of statistical computing and relevant resources in the CMU Department of Statistics and Data Science. | ||
== Implementation == | |||
The main aim for statistical computing is to implement proposed algorithms in code such that they can be applied to a variety of inputs. These implmentations can be as simple as computing the OLS coefficients to optimizing a billion-parameter neural network. | |||
== Scale and Efficiency == | |||
== Reproducibility == | == Reproducibility == | ||
Revision as of 22:32, 11 March 2026
While the foundations of statistical methods are grounded in mathematical theory, we often need to demonstrate that these methods are useful in practice. This can done via toy examples, simulation studies, and applications to real-world datasets. We do not recommend spending hundreds of hours running calculations on datasets by hand. Thus, we will introduce the reader to core concepts of statistical computing and relevant resources in the CMU Department of Statistics and Data Science.
Implementation
The main aim for statistical computing is to implement proposed algorithms in code such that they can be applied to a variety of inputs. These implmentations can be as simple as computing the OLS coefficients to optimizing a billion-parameter neural network.
