The error term, or residual, in a regression model is the difference between the observed value and the predicted value. It represents the portion of the dependent variable that cannot be explained by the independent variable(s).

The Chi-Square Test is a common statistical test that is considered non-parametric. This test is often used to analyze categorical data and does not require assumptions about the population distribution.

Variance is a measure of dispersion that considers all data points in the dataset. It is calculated by taking the average of the squared differences from the mean.

The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.

A 95% confidence interval means that if we were to take a large number of samples and calculate the confidence interval for each sample, we would expect the true population parameter to fall within the interval 95% of the time.

Distance measures, like Euclidean distance or Manhattan distance, play a crucial role in cluster analysis. They determine the similarities or differences between data points. They influence how the clusters will be formed, as the most similar or closest data points get clustered together.

Spearman's rank correlation coefficient is a nonparametric measure of rank correlation. It's preferred over Pearson's correlation when the variables are not normally distributed, the relationship is nonlinear, or the data contains outliers. It assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables.

In the context of regression analysis, the difference between the observed value and the predicted value of the response variable is called a "residual".

A residual is the difference between the observed value of the dependent variable (y) and the predicted value (ŷ), given by the regression model. It represents the error of the estimate.

The correlation coefficient is not affected by changes in the center (mean) or scale (standard deviation) of the variables. This is because correlation measures the strength of a relationship between variables relative to their variability. It's a dimensionless quantity, so changes in the scale of measurements of the variables do not change it.