penerapan PCA dengan K means Clustering pada data Auto MPG

Nama: Sayla Dwi Natasya NIM: 3337260166 Prodi: Informatika Mata Kuliah: Statistika (A26)

En la Práctica 1 comenzamos a trabajar con la base de la Encuesta Nacional de Bienestar Autorreportado (ENBIARE) 2025 correspondiente a la entidad seleccionada por cada estudiante. En esta segunda práctica continuaremos trabajando con la misma entidad y la misma base. El objetivo será dar un paso adicional: pasar de describir una sola variable a comparar grupos de población.

This shows the output of mean comparison functions from the package rwf: report_ttests and report_wtests. Both functions run pairwise comparisons between the levels of each independent variable and return a table with test statistics, effect sizes, confidence intervals, and Bonferroni-adjusted significance flags.

This shows the output of Thurstonian IRT functions from the package rwf, for forced-choice and ranking questionnaires where respondents rank or compare items within blocks instead of rating each item independently. The functions cover the full workflow: building the comparison design, encoding rank data into the binary pairwise-comparison format the model needs, a from-scratch single-dimension item characteristic curve and grid-based ability estimator, a MAP/Empirical-Bayes-Modal scorer that reproduces thurstonianIRT::predict() directly from a fitted lavaan model’s parameters, and a Heywood-case diagnostic for the lavaan fit.

This shows the output of unidimensional IRT reporting functions from the package rwf. The functions are plot_irt_onefactor (test information and expected score curves) and report_irt (a full model report: coefficients, local independence diagnostics, absolute and relative fit statistics). Both use models fit with the mirt package.

This shows the output of the Hierarchical Linear Regression (HLR) function report_hlr from the package rwf. HLR builds regression models in sequential steps (blocks), adding predictors at each step. This shows how much additional variance each new set of predictors explains beyond the earlier ones, measured as the change in R² (ΔR²).

This shows the output of Exploratory Factor Analysis (EFA) functions from the package rwf. EFA uncovers the latent structure underlying a set of observed variables: it identifies groups of variables that share common variance, which are interpreted as factors. The functions include loading plots, scree plots, loading tables, residual diagnostics, and a full EFA report.

This shows the output of correlation functions from the package rwf. The functions include a correlation heatmap plotter, power analysis for correlation tests, a full bivariate correlation report (r, p, CI, adjusted p and sample size, all as lower-triangle matrices), and special correlation types for dichotomous and ordinal data (tetrachoric, polychoric, biserial, polyserial).

This shows the output of ANOVA-related functions from the package rwf. The functions cover non-parametric alternatives (Kruskal-Wallis), one-way and factorial ANOVA, MANOVA, effect size estimation, and post hoc comparisons. Each section shows rwf output alongside equivalent results from established packages so you can verify the numbers.