The range of values around the point estimate that captures the true population parameter at some predetermined confidence level is called a confidence interval. Confidence intervals are used in statistics to indicate the reliability of an estimate.

To prevent overfitting, we can apply a technique called regularization in polynomial regression. Regularization involves adding a penalty term to the loss function during the process of training a model. This penalty term discourages the coefficients of the model from reaching large values, leading to a simpler model that's less likely to overfit.

The primary purpose of an ANOVA (Analysis of Variance) test is to compare the means of three or more groups to see if they are different. It is a way to find out if survey or experiment results are significant. In other words, they help to figure out if you need to reject the null hypothesis or accept the alternate hypothesis.

The Addition Rule in probability is used to calculate the probability that at least one of two (or more) events will occur. It states that the probability of the occurrence of at least one of two mutually exclusive events is the sum of their individual probabilities.

The Law of Large Numbers states that as a sample size grows, the sample mean will be closer to the population mean. This means that the larger the sample size, the more the sample mean approximates the population mean.

It is more appropriate to use a two-way ANOVA instead of a one-way ANOVA when there are two independent variables. Two-way ANOVA is used to observe the interaction effect between the two independent variables on the dependent variable.

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 geometric mean is particularly useful when comparing different items with very different ranges. It is used in various kinds of growth rates, like population growth or financial growth, where each year's value is relative to the previous year's value.

The result of a Kruskal-Wallis Test is interpreted as a measure of difference between groups. If the test is significant, it suggests that at least one of the groups differs from the others.

Multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. This can lead to unstable estimates of the coefficients.