Outliers can have a big impact on the mean. Depending on whether the outlier is much higher or lower than the other values, it can significantly increase or decrease the mean, thereby skewing the data.

The wrangling phase of the EDA process can be challenging as it involves dealing with various data quality issues. These can include missing values, inconsistent data entries, outliers, and other anomalies. The analyst might need to make informed decisions about how to handle these issues without introducing bias or distorting the underlying information in the data.

The choice of model in a model-based method can significantly influence the accuracy of the imputations. If the chosen model closely matches the actual data generation process, then the imputations will be accurate. However, if the model is a poor fit, the imputed values may be far from the true values, leading to biased results.

Predictive Modeling would be most suitable in this case. It involves the application of machine learning algorithms to the data in order to make predictions about future outcomes, in this case, customer churn.

MAR, which stands for Missing At Random, implies that the probability of missingness is related to the observed data.

Pairplots are primarily used for comparing multiple variables at once. It creates a grid of scatter plots for each pair of variables, which helps in understanding the relationships between all variables.

The first step in the EDA process, 'questioning,' involves formulating the questions that you want to answer with your data. It's during this step that you define what you want to achieve with your analysis and what problems you are trying to solve.

NMAR (Not Missing At Random) is typically observed when the missingness is related to the value of the missing data itself. This is the most challenging type of missingness to handle as it relies on unobserved data.

Spearman's correlation is more appropriate when dealing with ordinal variables. Unlike Pearson's, Spearman's correlation works with ranks, which makes it suitable for ordinal data.

Anomalies or outliers in the dataset can be identified through the process of EDA. Various techniques such as visualization methods (like box plots and scatter plots) and statistical methods (like the IQR method or the Z-score method) can be used to detect outliers during EDA.

A line chart is the most suitable visualization for displaying trends over time, making it easy to observe how a specific metric, like monthly sales data, changes over a period. It connects data points with lines, allowing for a clear view of trends.

Stateful applications, unlike stateless ones, retain client session data. This can make load balancing complex because the session data must be maintained consistently, potentially limiting scalability. Stateful applications often require additional strategies for handling session data, making them more challenging in terms of scalability.