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.

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.

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.

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.

In a positively skewed distribution, the "Mean" is generally greater than the median. Positive skewness means that the distribution has a long right tail, so extreme values in the positive direction can pull the mean upwards more than the median.

A leptokurtic distribution has kurtosis greater than 0, indicating a sharper peak and fatter tails compared to a normal distribution.

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.

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.

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.

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.