Correlation Analysis Module

A measure of the strength and direction of a linear relationship between two variables.

A correlation test that measures the agreement between ranks or orders of two variables, as opposed to just the numerical values.

A correlation test that measures the monotonicity of a relationship between two variables, also looking at rank, rather than numerical values.

The probability of obtaining results as extreme as or more extreme than those observed, assuming there's no real relationship between variables.

Determining whether the result/relationship observed is probably to be due to chance.

A statistical analysis used to find if variables are correlated.

The probability of making a Type I error (concluding a relationship or difference exists when it doesn't).

The degree of certainty (95%) that the observed result is likely true and not due to chance only.

Quantifies the strength and direction of association between two variables. Doesn't imply causation.

One variable directly influences another, resulting in a change. It's a direct link.

Correlation shows a relationship, but doesn't prove one variable causes the other. Causation implies a direct influence.

Ice cream sales and drownings increase in summer, but not because one causes the other. There's a third factor: heat.

Correlation analysis doesn't reveal if one variable causes another. It only shows association.

The threshold probability for rejecting the null hypothesis. A lower alpha level demands stronger evidence.

Predicts the relationship between variables, exploring causality. Goes beyond just correlation.

A statistical test used to determine if a dataset comes from a normal distribution. It helps assess if the data follows a bell-shaped curve.

A specific test for normality that compares the distribution of the data to a normal distribution. It assesses the maximum difference between the data and the expected normal distribution.

A test for normality that focuses on the skewness and kurtosis of the data. It checks how much the data deviates from a normal distribution's symmetric shape.

A normality test that specifically assesses how closely the data's distribution matches a normal distribution. It's widely used to check the normality assumption for statistical tests.

A normality test that specifically assesses how closely the data's distribution matches a normal distribution, especially when the data values are already known.

A normality test that assesses if the data's skewness and kurtosis deviate significantly from a normal distribution. It checks for non-normal features through these characteristics.