This repository contains documentation and summary reports showcasing the application of statistical analysis in the fields of Science, Technology, Engineering, and Mathematics (STEM). The purpose of this repository is to demonstrate the use of statistical methods and tools in conducting and interpreting research and analysis in these disciplines.
The materials in this repository are primarily focused on providing insights into how statistical techniques are applied in real-world STEM scenarios. These include case studies, research findings, and analytical reports that highlight the role of statistics in solving complex problems and making informed decisions.
The purpose of this repository is to showcase my skills in utilizing Jupyter notebooks and Python packages for effective statistical analysis in STEM. It also serves to show my experience and ability to apply statistical concepts and techniques to analyze and interpret data. By providing detailed reports and summaries of statistical analysis in various STEM fields, this repository demonstrates my proficiency in conducting research, drawing insights, and making informed decisions based on data.
The repository contains a set of analysis reports created using Jupyter and python libraries, and are structured to cover various topics and applications of statistics in STEM:
Summarizing and describing data characteristics. Examples include analysis of temperature variations in climate studies and measurement accuracy in engineering experiments.
Understanding the likelihood and uncertainty of events. This section covers applications such as modeling the probability of genetic mutations in biology and assessing risk factors in environmental studies.
Drawing conclusions and making predictions from data. Reports in this section demonstrate techniques like estimating population parameters in ecology and analyzing clinical trial results in medical research.
Investigating relationships between variables. Case studies include exploring the correlation between pollutants and health outcomes in public health research and modeling the impact of physical properties on material strength in materials science.
Hypothesis testing is a statistical method used to make decisions or inferences about a population based on sample data. It is a fundamental aspect of statistical analysis that allows researchers to test assumptions, theories, or hypotheses about a parameter or distribution. The documents and reports in this section illustrate how hypothesis testing is applied in different STEM scenarios. They include case studies, experiments, and research findings that employ various hypothesis testing techniques, such as t-tests, chi-square tests, ANOVA, and regression analysis.
If you run into any encrypted source folders and need to access any of the source files, please reach out to me at: ryanshatch.
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