MATH485 — Probability and Statistical Inference II

Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

Monitor clinical trials or experiments to ensure adherence to established procedures or to verify the quality of data collected.

Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.

Develop and test experimental designs, sampling techniques, and analytical methods.

Prepare data analysis listings and activity, performance, or progress reports.

Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.

Report results of statistical analyses, including information in the form of graphs, charts, and tables.

Identify relationships and trends or any factors that could affect the results of research.

Develop or implement data analysis algorithms.

Analyze clinical data using appropriate statistical tools.

Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.

Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.

Perform computations and apply methods of numerical analysis to data.

Develop or implement data analysis algorithms.

Develop or apply data mining and machine learning algorithms.

Develop methods for integrating different products so they work properly together, such as customizing commercial databases to fit specific needs.

Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.

Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.

Identify business problems or management objectives that can be addressed through data analysis.

Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

Perform computations and apply methods of numerical analysis to data.

Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data.

Determine whether statistical methods are appropriate, based on user needs or research questions of interest.

Specify manipulative or computational methods to be applied to models.

Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.

Select methods, techniques, or criteria for data warehousing evaluative procedures.

Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.

Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.

Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.

Develop and test experimental designs, sampling techniques, and analytical methods.

Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.

Design research projects that apply valid scientific techniques, and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.

Analyze, manipulate, or process large sets of data using statistical software.

Determine whether statistical methods are appropriate, based on user needs or research questions of interest.

Collect data through surveys or experimentation.

Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.

Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.

Design surveys, opinion polls, or other instruments to collect data.

Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.