Traducción y análisis de palabras por inteligencia artificial ChatGPT
En esta página puede obtener un análisis detallado de una palabra o frase, producido utilizando la mejor tecnología de inteligencia artificial hasta la fecha:
- cómo se usa la palabra
- frecuencia de uso
- se utiliza con más frecuencia en el habla oral o escrita
- opciones de traducción
- ejemplos de uso (varias frases con traducción)
- etimología
Qué (quién) es exploratory heading - definición
![variation that increases with tip amount]]. In particular, there are more points far away from the line in the lower right than in the upper left, indicating that more customers are very cheap than very generous.](https://commons.wikimedia.org/wiki/Special:FilePath/Tips-scat1.png?width=200 "variation that increases with tip amount]]. In particular, there are more points far away from the line in the lower right than in the upper left, indicating that more customers are very cheap than very generous.")
![A1 and A1X 'Terrier]]s' Wooton and Freshwater running around the train at [[Wootton railway station]], [[Isle of Wight Steam Railway]]](https://commons.wikimedia.org/wiki/Special:FilePath/Southern Railway Class A1X W11 and W8 Wootton.jpg?width=200 "A1 and A1X 'Terrier]]s' Wooton and Freshwater running around the train at [[Wootton railway station]], [[Isle of Wight Steam Railway]]")
![A double headed steam [[excursion train]] in [[Iowa]], September 2006](https://commons.wikimedia.org/wiki/Special:FilePath/SteamAcrossIowaRiver.JPG?width=200 "A double headed steam [[excursion train]] in [[Iowa]], September 2006")
Wikipedia
In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.
