In statistics, linear regression is a method of estimating the conditional expected value of one variable y given the values of some other variable or variables x.The Linear Regression Forecast indicators optionally smoothes the price data, performs a regression on the result, forecasts the regression lines if desired, and then optionally creates standard deviations bands above and below the regression line. First, the data, based on the price selected, is smoothed using the moving average period and type. The resulting data is then used to form regression lines ending at each bar, using the regression period specified. The values at each bar can optionally be forecasted values, determined by projecting the regression lines X bars into the future, X being the forecast period. If X=0, then no forecasting will occur. Standard deviation bands can then be drawn above and below the regression line, based on a number of standard deviations (standard deviation multiple) specified, and a standard deviation value computed using data in the the regression period range. Usage: It is use as the overall trend line for that given period & support and resistance level. It displays the statistical trend of a security's price over a specified time period. The trend is based on linear regression analysis. Linear Regression : This page is about the Linear Regression Channel. If you are interested in the Linear Regression Curve or Linear Regression Line please select the links below: Linear Regression Line Linear Regression Curve Linear Regression Channel : Similar to the 200-day Moving Average, large institutions often look at long term Linear Regression Channels. A Linear Regression Channel consists of three parts: 1. Linear Regression Line: A line that best fits all the data points of interest. For more information, see: Linear Regression Line. 2. Uppe r Channel Line: A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line. 3. Lower Channel Line: This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line. The multi- year chart of the S&P 500 exchange traded fund (SPY) shows prices in a steady uptrend and maintaining in a tight one standard deviation Linear Regression Channel: The upper and lower channel lines contain between themselves either 68% of all prices (if 1 standard deviation is used) or 95% of all prices (if 2 standard deviations are used). When prices break outside of the channels, either: 1. Buy or sell opportunities are present. 2. Or the prior trend could be ending. Linear Regression Channel Buy Signal : When price falls below the lower channel line, a buy signal is usually triggered. Linear Regression Channel Sell Signal :An opportunity for selling occurs when prices break above the upper channel line. Other confirmation signs like prices closing back inside the linear regression channel could be used to initiate buy or sell orders. Also, other technical indicators should be used to confirm. Trend Reversals : When price closes outside of the Linear Regression Channel for long periods of time, this is often interpreted as an early signal that the past price trend may be breaking and a significant reversal might be near. Linear Regression Channels are quite useful technical analysis charting tools. In addition to identifying trends and trend direction, the use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold relative to the long term trend. (1) Linear Regression Line : A Linear Regression Line is a straight line that best fits the prices between a starting price point and an ending price point. A "best fit" means that a line is constructed where there is the least amount of space between the price points and the actual Linear Regression Line. The Linear Regression Line is mainly used to determine trend direction. A chart of AT&T (T) stock is given below: Traders usually view the Linear Regression Line as the fair value price for the future, stock, or forex currency pair. When prices deviate above or below, traders expect prices to go back towards the Linear Regression Line. As a consequence, when prices are below the Linear Regression Line, this could be viewed as a good time to buy, and when prices are above the Linear Regression Line, a trader might sell. Of course other technical indicators would be used to confirm these inexact buy and sell signals. A useful technical analysis charting indicator that uses a Linear Regression Line is the Linear Regression Channel (see: Linear Regression Channel), which gives more objective buy and sell signals based on price volatility. (2)Linear Regression Curve: The Linear Regression Curve plots a line that best fits the prices specified over a user-defined time period. Think of the Linear Regression Curve as numerous lines, but both extreme ends of the lines are hidden, while the center portion is shown and is connected to other center portions of lines. The Linear Regression Curve is used mainly to identify trend direction and is sometimes used to generate buy and sell signals. The chart of the S&P 500 E- mini Futures contract shows a 9-day Linear Regression Curve: Many traders view the Linear Regression curve as the fair value for the stock, future, or forex currency pair, and any deviations from the curve as buy and sell opportunities. Generally, when price deviates a certain percentage or number of points below the Linear Regression Curve, then a trader buys, thinking that price will revert back to fair value, which is thought to be the Linear Regression Curve. In a similar manner, when price moves above the Linear Regression Curve by a trader specified percentage or point value, then the trader will sell, believing that price will return back to the Linear Regression Curve. Other variations of these buy and sell signals could be employed. Since the Linear Regression Curve is great at identifying trend direction, if price is trending higher, a trader co uld only take buy signals when price deviated below the curve. Likewise, during a downtrend, a trader might only take sell signals, not wanting to fight the prevailing trend downward. Arguably the most popular usage of the Linear Regression concept is the Linear Regression Channel, often used by large institutions. (see: Linear Regression Channel).