for Envista

Chapter 5 discusses regression diagnostics, how they provide information about the reasonableness of model assumptions and possible remedies.

This project provides a comprehensive framework for data preparation and analysis, beginning with missing data handling, outlier detection, and reliability checks for Likert scale data. It then applies a range of statistical methods, including descriptive statistics, correlation, bivariate linear regression, chi-square tests, t-tests with effect sizes, and both parametric and nonparametric comparisons, such as ANOVA and the Kruskal-Wallis test. Supporting appendices detail test development, math anxiety measurement, statistical assumptions, diagnostic tools, and effect size computation in R. Overall, the project integrates careful data screening with diverse inferential techniques to ensure robust and interpretable results in psychological and educational research.

This assignment covers tree-based classification models,

Pamela