The three main types of correlation are positive, negative and no correlation. A positive correlation means that both variables increase together. A negative correlation means that as one variable increases, the other decreases. No correlation means that the variables do not change with each other.
Scatter plots help show correlation between data. By placing one variable on the X-axis and the other on the Y-axis, it is possible to see how changes in one variable changes with the other. Scatter plots do not connect dots. The points on a scatter plot show either strong, weak or perfect correlation.
Perfect correlation means that all points on the scatter plot fall on the same line. Strong correlation means the points generally fall very close to the line. A weak correlation means that the points generally follow a trend, even though they do not all fall directly on the line.
Even though data shows a strong correlation, one variable does not necessarily cause the other. For example, a plot may show that there is a positive correlation between income and age. However, increasing age does not cause an increase in income; people with more experience have higher incomes and experience comes with age.