A Linear Regression trendline uses the least squares method to plot a straight line 
through prices so as to minimize the distances between the prices and the resulting 
trendline.
If you had to guess what a particular security's price would be tomorrow, a logical guess 
would be "fairly close to today's price."  If prices are trending up, a better guess might 
be "fairly close to today's price with an upward bias."  Linear regression analysis is the 
statistical confirmation of these logical assumptions.
A Linear Regression trendline is simply a trendline drawn between two points using the 
least squares fit method.  The trendline is displayed in the exact middle of the prices.  
If you think of this trendline as the "equilibrium" price, any move above or below the 
trendline indicates overzealous buyers or sellers.
A popular method of using the Linear Regression trendline is to construct Linear 
Regression Channel lines.  Developed by Gilbert Raff, the channel is constructed by 
plotting two parallel, equidistant lines above and below a Linear Regression trendline. The 
distance between the channel lines to the regression line is the greatest distance that any 
one closing price is from the regression line.  Regression Channels contain price movement, 
with the bottom channel line providing support and the top channel line providing 
resistance.  Prices may extend outside of the channel for a short period of time.  However 
if prices remain outside the channel for a longer period of time, a reversal in trend may 
be imminent.
A Linear Regression trendline shows where equilibrium exists.  Linear Regression 
Channels show the range prices can be expected to deviate from a Linear Regression 
trendline.
The Time Series Forecast indicator displays the same information as a 
Linear Regression trendline.  Any point along the Time Series Forecast 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 Time Series 
Forecast.