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 choice of the model in a model-based imputation method directly affects the accuracy of the imputation process. If the chosen model does not accurately reflect the true data generation process, the imputed values may be biased, leading to incorrect conclusions.

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.

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.

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.