The Linear Regression indicator is based on the trend of a instrument's price over a specified time period. The trend is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.

Any point along the Linear Regression indicator is equal to the ending value of a Linear Regression trendline. For example, the ending value of a Linear Regression trendline that covers 10 days will have the same value as a 10-day Linear Regression indicator. This differs slightly from the Time Series Forecast indicator (see Rather than plotting a straight Linear Regression trendline, the Linear Regression indicator plots the ending values of multiple Linear Regression trendlines.

Linear Regression Slope

The Linear Regression method provides several useful outputs for technical analysts, including the Slope. The Slope shows how much prices are expected to change per unit of time. Some may remember this as "rise over run."

Correlation

The purpose of correlation analysis is to measure the relationship between two variables. This relationship is called the "correlation coefficient."

The correlation coefficient ranges between ±1.0. A coefficient of +1.0 is a perfect positive correlation and -1.0 is a perfect negative correlation. Two variables with no relationship will have a coefficient of zero.