Ridge regression introduces a regularization term (penalty term) into the loss function which helps to shrink the coefficients towards zero and mitigate the effect of multicollinearity.

Adding more predictors to a model may increase the R-squared value, making it appear that the model is improving. However, if these additional predictors are not truly associated with the response variable, it may result in overfitting, making the model perform poorly on new, unseen data.

In the context of probability, an outcome refers to a possible result of a random experiment. For example, if the experiment is tossing a coin, the possible outcomes are 'Heads' or 'Tails'. Each outcome is considered mutually exclusive, meaning only one outcome can occur at a time.

Multicollinearity is a problem that can occur in multiple linear regression when two or more predictor variables are highly correlated with each other. This can lead to unstable estimates of the regression coefficients and make it difficult to determine the individual effects of the predictor variables.

In a multiple linear regression equation, the regression coefficient represents the expected change in the dependent variable for a one-unit change in the corresponding independent variable, while holding all other independent variables constant. It gives the direction and strength of the relationship between the dependent variable and each independent variable.

Multicollinearity can inflate the variance of the regression coefficients, thus widening the confidence intervals and reducing the power of the statistical test.

The purpose of point estimation in statistics is to provide a single "best guess" or "most likely" value for an unknown parameter of a population, such as the mean or the proportion. It's a single value that approximates an unknown parameter based on sampled data.

In probability, two events are independent if the occurrence of one event does not affect the occurrence of the other. This means that the probability of both events occurring is the product of their individual probabilities.

A Chi-square test for independence can be used in feature selection by identifying the features that have a significant association with the target variable.

Spearman's Rank Correlation is based on the ranks of the data rather than their raw values, which makes it a non-parametric method.

A Type II error occurs when we fail to reject the null hypothesis, even though it is false. This is also known as a "false negative" error.

The type of data that describes attributes or characteristics of a group is called Qualitative data. These are often non-numeric and may include data types such as text, audio, or video. Examples include a person's gender, eye color, or the make of a car.