In this case, the IQR method is a better choice as it does not assume any specific data distribution unlike the Z-score method, which assumes data is normally distributed.

If a row in a heatmap has colors that are drastically different than the rest, it might indicate that the corresponding variable is unrelated or has very different relationships with the other variables in the dataset.

The "Standard Deviation" in a sample differs from that in a population in the way it is calculated. For a sample, the denominator is (n-1) instead of n, which is Bessel's correction to account for sample bias.

A correlation coefficient close to 0 indicates that there is no linear relationship between the two variables. This means that changes in one variable are not consistently associated with changes in the other variable. It does not necessarily mean that there is no relationship at all, as there may be a non-linear relationship.

Once the data has been 'wrangled' i.e., cleaned and transformed, the next step in the EDA process is 'exploring'. This stage involves examining the data through statistical analysis and visual methods.

Mode imputation might not be the best strategy for a dataset with a categorical variable missing for some rows. Although it's simple to implement, it may fail to capture the underlying data pattern, introduce bias if the data is not missing at random, and distort the original data distribution by overrepresenting the mode.

The process of removing an entire row when any single data point within it is missing is called 'Listwise Deletion'. Also known as 'Complete Case Analysis', this technique is straightforward and fast, but it can potentially discard valuable data and introduce bias if the missingness is not completely at random.

While Matplotlib is a powerful library for creating a wide range of plots, Seaborn adds on to this by providing a number of high-level statistical plotting capabilities, allowing users to create more informative and attractive visualizations with fewer lines of code.

The "Mode" is the measure of central tendency that can be used for both quantitative and qualitative data. It is the value that appears most frequently in a data set, and it is the only measure of central tendency that can be used with nominal data.

Mean/Median/Mode Imputation might introduce bias if the data is not missing completely at random. If missing values have some systematic patterns, replacing them with mean, median, or mode might lead to incorrect estimation of variability and biased results.