Transformations, such as logarithmic or square root transformations, can help in reducing skewness by making the distribution more symmetric. The choice of transformation often depends on the degree and direction of skewness.

Multicollinearity is diagnosed in a multiple linear regression model by checking the correlation matrix and the Variance Inflation Factor (VIF). A high correlation between independent variables and a VIF greater than 5 or 10 suggests the presence of multicollinearity.

According to the empirical rule (also known as the 68-95-99.7 rule), in a normal distribution, about 68% of the data lies within one standard deviation of the mean, about 95% lies within two standard deviations, and about 99.7% lies within three standard deviations.

A Pearson correlation coefficient of 0 indicates no correlation. This means that the variables are independent and there is no linear relationship between them.

In ANOVA, if the null hypothesis is true, the F-statistic follows an F-distribution. The F-distribution is a probability distribution that is used most commonly in Analysis of Variance.

Spearman's Rank Correlation is especially useful when the relationship between variables is monotonic, but not necessarily linear. A monotonic relationship is one where the variables tend to change together, but not necessarily at a constant rate.

Interval estimation is generally preferred over point estimation because it provides a range of possible values rather than a single value. This range of values gives a better understanding of the uncertainty around the estimate, hence, it provides more information than a single point estimate.

The Wilcoxon Signed Rank test is a non-parametric test that compares the medians of two paired groups.

The support of a random variable is the set of values in the range of the variable that have non-zero probability. For a continuous random variable, it's the set of values over which the probability density function is non-zero.

The 'R-squared' value, also known as the coefficient of determination, in a multiple linear regression model represents the proportion of variance in the dependent variable that can be predicted from the independent variables. It ranges from 0 to 1, where a higher value indicates a better fit of the model.

The difference between kurtosis and excess kurtosis comes down to a constant. Excess kurtosis is simply kurtosis minus 3. The "3" comes from the kurtosis of a normal distribution which is 3. Hence, excess kurtosis refers to kurtosis in relation to a normal distribution.

In an ANOVA test, the F-statistic is calculated as the ratio of the between-group variance (mean sum of squares between groups) to the within-group variance (mean sum of squares within groups). A larger F-statistic implies a greater degree of difference between the group means.