- Coding Statistical Algorithms (k4)
Students can implement algorithms for computational statistics, such as non-linear optimization, root finding, and pseudorandom number generation, in a programming language.
- Applying Statistical Methods to Data Sets (k4)
Students are able to apply numerical methods like the EM algorithm, Jackknife, and bootstrap on data sets, performing analyses to extract meaningful statistical inferences.
- Conducting and Interpreting Regression Analyses (k4)
Students can execute regression analyses on various data sets and interpret the results in the context of the underlying statistical models.
- Performing Permutation Tests and Numerical Integrations (k4)
Students are capable of performing permutation tests to assess statistical significance and applying numerical integration methods in computational scenarios.
- Ensuring Accuracy and Precision in Computation (k5)
Students critically evaluate the precision of their computations, addressing potential issues with computer arithmetic and verifying the correctness of their statistical results.
|
Students have practical knowledge in implementing and applying computational statistical methods through coding exercises, focusing on topics like optimization, statistical precision, and regression. They also know how to handle real-world data sets, perform detailed statistical analyses, and ensure the accuracy of their computational results through practical application of theoretical concepts.
|
