The standard deviation and mean absolute deviation both measure the dispersion in a dataset. The key difference lies in how they treat deviations from the mean: standard deviation squares the deviations before averaging them, while mean absolute deviation takes the absolute value of deviations before averaging. As a result, standard deviation is more sensitive to extreme values than the mean absolute deviation.

Quantitative data represents quantities and can be measured on a Numerical scale. It includes both discrete data (e.g., the number of students in a class) and continuous data (e.g., the weight of a person).

Pearson's correlation coefficient (denoted as r) is a measure of the strength and direction of association that exists between two continuous variables. It measures the degree to which pairs of data for these two variables lie on a line. The values lie between -1 and 1, where 1 indicates a perfect positive correlation, -1 a perfect negative correlation, and 0 no correlation at all.

If the population standard deviation is unknown, the sample standard deviation is used to estimate the standard error of the mean. The standard error is a measure of how much the sample mean is expected to vary from the true population mean.

If the F statistic in an ANOVA is significantly high, it suggests that the null hypothesis should be rejected. The null hypothesis in ANOVA is typically that all group means are equal.

A uniform distribution, also called a rectangular distribution, is a type of probability distribution in which all outcomes are equally likely. Each interval of equal length on the distribution's support has the same probability.

The null hypothesis for the Kruskal-Wallis Test states that all groups have the same distribution. It tests whether samples originate from the same distribution.

The correlation coefficient remains the same when you switch the X and Y variables. This is because correlation measures the strength and direction of a relationship between two variables, not the dependency of one on the other.

In multiple linear regression, multicollinearity refers to a situation in which two or more independent variables are highly linearly related. This can cause problems because it can affect the interpretability of the regression coefficients and can make the model unstable.

The expected value or expectation of a random variable is a key concept in probability and statistics and represents the weighted average of all possible values that the variable can take, with weights being the respective probabilities.