Write a R program to create multiple dot plots for grouped data comparing the distribution of variable across different categories using ggplot’2 position_dodge function.

Write an R program to create multiple dot plots or group data , comparing the distributions of variables across different categories, using ggplot2's position_dodge function.

Write an R program to create multiple dot plots or group data , comparing the distribution of variables across different categories, using ggplot2's position_dodge function.

Write a R program to create many dotplots from the grouped data.comparing the distribution of varaiables across using ggplot2’s dodge position function

Semester 4 Nama: Cindy Pramudita NIM: 2304220040 Prodi: Statistika dan Sains Data MK: Pemodelan Statistika dan Simulasi

Write an R program to create a multiple dot plot or grouped data compairing the distance of variables across different categories using ggplot2's position_dodgefunction.

Write an R program to create many dotplots from the grouped data, comparing the distributions of variables across using ggplot2’s position_dodge functions.

Write an R program to create many dotplots from grouped data. Comparing the distributions of variables across using ggplot2 's position_dodge function.

Write an R program to create many dotplots from grouped data.Comparing the distributions of variables across using ggplot2’s position_dodge::function.

La mappa interattiva rappresenta il tasso di sovraffollamento delle carceri nelle diverse province italiane, calcolato come rapporto percentuale tra la capienza regolamentare degli istituti penitenziari e il numero di detenuti presenti (*100). Le province con un livello critico di sovraffollamento sono evidenziate in rosso, mentre quelle non sature appaiono in verde. Passando il cursore su ciascuna provincia, è possibile visualizzare il nome e il valore del tasso di sovraffollamento, permettendo un'analisi immediata delle aree più problematiche.

The purpose of this publication is to explore the application of multinomial logistic regression in predicting the type of glass based on chemical composition features. The study aims to analyze the influence of various chemical properties, such as RI, Na, Mg, Al, and Ca, on the differentiation between different glass types. By understanding the predictive power of these variables, the research seeks to contribute to the field of material classification and enhance the accuracy of predictions for industrial processes related to glass production. Additionally, this publication addresses the challenges faced in classifying certain glass types, such as "vehicle_float" and "headlamps," and provides insights into potential improvements for model performance.