Analisis Faktor-Faktor yang Mempengaruhi Konsentrasi PM2.5 Menggunakan Regresi Linier Berganda (Publikasi ini dibuat guna syarat Ujian Akhir Semester)

Typing on mobile devices can be slow and tedious. Our goal was to create a data product that: - Predicts the next word in a phrase instantly. - Reduces user keystrokes for better efficiency. - Operates with high speed and low memory usage.

En esta sección realizamos el Análisis Factorial Exploratorio (AFE). El objetivo es verificar que los ítems carguen correctamente en sus dimensiones teóricas antes de proceder al modelo estructural.

NAMA: AMANDA DEVINA DWI YULIANTI NIM: B2A023006 UJIAN AKHIR SEMESTER KOMPUTASI STATISTIKA 2 PROGRAM STUDI STATISTIKA FAKULTAS SAINS DAN TEKNOLOGI PERTANIAN UNIVERSITAS MUHAMMADIYAH SEMARANG

# Findings and Conclusions After processing the raw data and using the ARIMA package to model ride-share data, I was able to make predictions for the 25 days beyond the current data set. Qualitatively the data shows that as the weather gets warmer, the number of bike rentals increases, and over the course of two years, the number of rentals increases over the number of rentals from the previous year. As the data terminates at the end of one cycle, I expect the number of rentals to increase to a level higher than it was a year before, which is what the models are predicting. Therefore the results were what I expected the data appears to oscillate up and down over a 1-year period, with the overall data moving towards higher rental numbers.

Gradient Boosting com Features Temporais

Forecasting Robusto com Detecção de Change Points