A negative correlation coefficient used to describe the extent that two variables move in opposite directions. For example, with the two variables, X and Y, an increase in X is associated with a decrease in Y. A negative correlation coefficient is also referred to as an inverse correlation.
A negative correlation demonstrates a connection between two variables in the same way a positive correlation coefficient does, and the relative strengths are the same. In other words, a correlation coefficient of 0.85 shows the same strength as a correlation coefficient of -0.85.
Correlation coefficients are always values between -1 and 1, where -1 shows a perfect, linear negative correlation, and 1 shows a perfect, linear positive correlation. Another way of thinking about the numeric value of a correlation coefficient is to imagine it's a percentage. A 20% move higher for variable X would equate to a 20% move lower for variable Y.
A correlation coefficient of zero, or very close to zero, shows no meaningful relationship between variables. In reality, these numbers are rarely seen, as there are very few perfectly linear relationships.
An example of a strong negative correlation would be -.97 whereby the variables would move in opposite directions in a nearly identical move. As the numbers approach 1 or -1, the values demonstrate the strength of a relationship; for example, 0.92 or -0.97 would show, respectively, a very strong positive and negative correlation.
For example, as the temperature increases outside, the amount of snowfall decreases; this shows a negative correlation and would, by extension, have a negative correlation coefficient.
A positive correlation coefficient would be the relationship between temperature and ice cream sales; as temperature increases, so too do ice cream sales. This relationship would have a positive correlation coefficient. A relationship with a correlation coefficient of zero, or very close to zero, might be temperature and fast food sales (assuming there's zero correlation for illustrative purposes).