Linear Regression & Best Line Analysis
Linear Regression & Best Line Analysis
Linear regression is used to make predictions about a single value. Linear regression involves discovering the equation for a line that most nearly fits the given data. That linear equation is then used to predict values for the data. A popular method of using the Linear Regression is to construct Linear Regression Channel lines. Developed by Gilbert Raff, the channel is constructed by plotting two parallel, middle lines above and below a Linear Regression trend line. Regression Channels contain data movement, with the bottom channel line providing support and the top channel line providing resistance. Data may extend outside of the channel for a short period of time. However if the data remains outside the channel for a longer period of time, a reversal in trend may be coming up.
A Linear Regression trend line shows where stability exists. Linear Regression Channels show the range of data can be expected to stray from a Linear Regression trend line. These two lines form a linear regression channel. The two lines act as support and resistance. Once the lines are broken for a sustained period of time, this is an indication that the trend has reversed or gained great momentum. A stock which is moving slightly upward or downward for a period of time which suddenly moves outside the channel in the same direction of the previous move, is showing signs that it will continue the move. A stock which was trending upward or downward and has changed direction and broken the opposite channel for a continued period is showing signs that the trend will probably continue. The space inside the channel is where stability exists. This is the area in which data can be expected to deviate from the original linear regression line. What I have found is that generally, when data moves outside or to the extreme channel line, data tends to move back to the opposite channel
Linear regression is used to make predictions about a single value. Linear regression involves discovering the equation for a line that most nearly fits the given data. That linear equation is then used to predict values for the data. A popular method of using the Linear Regression is to construct Linear Regression Channel lines. Developed by Gilbert Raff, the channel is constructed by plotting two parallel, middle lines above and below a Linear Regression trend line. Regression Channels contain data movement, with the bottom channel line providing support and the top channel line providing resistance. Data may extend outside of the channel for a short period of time. However if the data remains outside the channel for a longer period of time, a reversal in trend may be coming up.
A Linear Regression trend line shows where stability exists. Linear Regression Channels show the range of data can be expected to stray from a Linear Regression trend line. These two lines form a linear regression channel. The two lines act as support and resistance. Once the lines are broken for a sustained period of time, this is an indication that the trend has reversed or gained great momentum. A stock which is moving slightly upward or downward for a period of time which suddenly moves outside the channel in the same direction of the previous move, is showing signs that it will continue the move. A stock which was trending upward or downward and has changed direction and broken the opposite channel for a continued period is showing signs that the trend will probably continue. The space inside the channel is where stability exists. This is the area in which data can be expected to deviate from the original linear regression line. What I have found is that generally, when data moves outside or to the extreme channel line, data tends to move back to the opposite channel