A significance map is a representation of positions of significant transform coefficients. In adaptive coding schemes, this map changes with the sample functions and a large fraction of code bits are used to encode it in low bit rate situations.

P values and coefficients in regression analysis work together to tell you which relationships in your model are statistically significant and the nature of those relationships. The linear regression coefficients describe the mathematical relationship between each independent variable and the dependent variable.

The significance map shows the locations with a significant local statistic, with the degree of significance reflected in increasingly darker shades of green. The map starts with p < 0.05 and shows all the categories of significance that are meaningful for the given number of permutations.

This post will define correlation, types of correlation, explain how to measure correlation using correlation coefficient, and especially how to assess the reliability of a linear correlation using a significance test.

We perform a hypothesis test of the "significance of the correlation coefficient" to decide whether the linear relationship in the sample data is strong enough to use to model the relationship in the population. The sample data are used to compute r, the correlation coefficient for the sample.