Linear models are more susceptible to the issue of feature redundancy as they assume independence among features. Redundant features violate this assumption and can cause problems.

'Mode Imputation' is a method that is typically used to handle missing categorical data. It fills missing values with the mode (most common value) of the available data. While it's a simple and fast method, it could introduce bias if the data is not missing completely at random.

The parameters of a Uniform Distribution are typically defined as 'a' and 'b', representing the minimum and maximum values respectively.

When presenting complex data to non-technical stakeholders, it's best to use simple and familiar graph types, use clear labels and legends, and choose a thoughtful color scheme. Avoid using complex graphs that the audience might not understand, and remember that your goal is to make the data understandable, not to show off your technical skills.

The term "Central Tendency" is used to describe the measure of the center of a distribution of data. Measures of central tendency aim to describe the central position of a distribution for a data set where the most typical values lie.

When interpretability is crucial, such as in a medical diagnosis problem, it's important to select the features that contribute most to the model's performance. This can make the model more understandable and transparent, which is important in fields where decisions have significant impacts and need to be explained.

The Min-Max scaling technique, which scales the data to a fixed range (usually 0 to 1), is highly sensitive to the presence of outliers. It shrinks the range of the feature values, so the outliers can drastically change the ranges of the attributes.

The process of converting an actual range of values in a numeric feature column into a standard range of values is known as Data Scaling. This is a fundamental step in data preprocessing, particularly important when dealing with machine learning algorithms.

Principal Component Analysis (PCA) is a dimensionality reduction technique that can be used when dealing with high-dimensional datasets with many redundant features. PCA transforms the original features into a new set of uncorrelated features, capturing the most variance in the data, thus helping to maintain the essential information while reducing the dimensionality.

In a normal distribution, the "Mean", "Median", and "Mode" are all the "Same", falling at the center of the distribution.