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A correlation is a relation between two data sets. For example, consider two data sets, one consisting of temperatures and the other consisting of the number of coats sold. A decrease in the temperature may imply an increase in the number of coats sold. Based on the trend of the bivariate data, three types of correlations are possible which can be described using scatter plots.
Positive Correlation: As x increases, y also increases (Scatter plot with points near to a non-visible line with positive slope); Negative Correlation: As x increases, y also decreases (Scatter plot with points near to a non-visible line with negative slope); No Correlation: There is no relationship between data sets, resulting in a random pattern in the scatter plot (Scatter plot with points points at random positions).
Knowing the type of correlation helps analyze trends and make predictions based on data. Furthermore, the shape of the patterns formed by positive and negative correlations can be thought to have a positive and negative slope, respectively. The applet below shows how a data set transforms from a random pattern to a positive or a negative correlation.