A MODEL CHANNEL OF LINEAR REGRESSION
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Linear regression is a statistical tool used to predict future values from past values. In the case of security prices, it is commonly used to resolute when prices are over extended.
A Linear Regression trend line uses the least squares methodology to devise a straight line through prices so as to minimize the distances between the prices and the resulting trend line.
A Linear Regression trend line is simply a trend line drawn between two points using the least squares fit method. The trend lines are displayed exactly in the which is middle of the prices. If you think of this trend line as the "equilibrium" price, any move above or below the trend line indicates enthusiastic buyers or sellers.
PRACTICAL USES OF LINEAR REGRESSION
The goal is prediction, forecasting, depletion of linear regression can be used to fit a predictive model to observe the values of the response and explanatory variables. Developing such a model, if additional values of the explanatory variables are collected without an escorting response value, the fitted model can be used to make a prediction of the response.
To interpret variation in the response variable that can be allocated to variation in the explanatory variables, linear regression analysis can be applied to quantify the power of the relationship between the response and the explanatory variables, and in particular to calculate whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
The 200-day Moving Average, Big institutions often look at long term Linear Regression Channels consisting of three parts.
Linear Regression Line: A line that best fits all the data points of interest.
Upper Channel Line: Line which runs parallel to the Linear Regression Line is usually one to two standard deviations above the Linear Regression Line.
Lower Channel Line: This line runs parallel to the Linear Regression Line and is very much one to two standard deviations below the Linear Regression line.
This is the popular method of using the Linear Regression trend line is to construct Linear Regression Channel lines which is Developed by Gilbert Raff, the channel is constructed by arranging two parallel, equidistant lines above and below a Linear Regression trend line. 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 the channel for a short period of time. If prices remain outside the channel for a longer period of time, a reversal in trend may be prominent.A Linear Regression trend line always shows where equilibrium subsists. Linear Regression Channels show the range prices can be expected to diverge from a Linear Regression trend line.
If price closes outside of the Linear Regression Channel for long periods of time, often interpreted as an early signal that the past price trend may be breaking and a notable reversal might be very near.
Linear Regression Channels are completely useful technical analysis tools. Where identifying trends and trend direction, the use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold respective to the long term.
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