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.

In Bayes' Theorem, the posterior probability is the updated probability of an event after new evidence has been observed. It is calculated by multiplying the likelihood and the prior probability and then dividing by the probability of the evidence.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) differs from K-means and hierarchical clustering in that it can find arbitrarily shaped clusters, and it's less affected by outliers. It does not require the user to set the number of clusters a priori, but instead, it infers the number of clusters based on the data.

If the results of an ANOVA test are significant, post-hoc tests are often used to identify specifically which groups' means are different. These tests are performed after the ANOVA and help to avoid type I errors when making multiple comparisons.

The primary objective of cluster analysis is to group similar instances (observations, data points, etc.) into clusters.

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.

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.

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.

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).

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.

Even in the presence of multicollinearity, the least squares estimates of the regression coefficients are still unbiased. However, they are less precise and have high standard errors.

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.