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 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 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.

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.

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.

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

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.

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.

The assumption of normality in residual analysis states that if we draw a large number of samples and create a distribution of the sample means, this distribution will be well approximated by a normal distribution. This is necessary to make inferences about the regression coefficients and to calculate prediction intervals.