Outliers in normally distributed data can be a result of various factors such as data entry errors, measurement errors, or errors in data processing.

In Plotly, the 'Figure' object is the top-level container in which all plot-related attributes such as data and layout are stored.

EDA, particularly data visualization, plays a crucial role in understanding the underlying structure of data. Visual techniques such as histograms, scatterplots, or box plots, can uncover patterns, trends, relationships, or outliers that would remain hidden in raw, numerical data. Visual exploration can guide statistical analysis and predictive modeling by revealing the underlying structure and suggesting hypotheses.

Backward elimination in feature selection involves starting with all variables and then removing the least significant variables one by one. This process can be computationally expensive, especially when dealing with datasets with a large number of features.

The 'style' function in Seaborn is used to set the overall aesthetic look of the plot, including background color, grids, and spines. The 'context' function allows you to set the context parameters, which adjust the scale of the plot elements based on the context in which the plot will be presented (e.g., paper, notebook, talk, poster).

When applying regression imputation, both dependent and independent variables need to be taken into consideration. A regression model is built using the complete cases and then this model is used to predict the missing values in the incomplete cases. Therefore, it is important to carefully consider which variables to include in the regression model.

Transformation is appropriate to use as an outlier handling method when the outliers are extreme but legitimate data points that carry valuable information.

If one dataset has a higher range but a lower IQR than the other, it could suggest that "The one with the higher range has more outliers". The range is sensitive to extreme values, while the IQR focuses on the middle 50% of data and is not affected by outliers.

A primary assumption when using regression imputation is that the relationship between variables is linear. This is because regression imputation uses a regression model to predict missing values, and the basic form of regression models assumes a linear relationship between predictor and response variables.

The poor performance of the model might be due to outliers in the dataset. Outliers can have a significant impact on the performance of machine learning models.