Least squares is a common way to measure errors in statistical analysis. The least squares formula is best known for favoring things with a lot of small errors over those with a few large errors.
FIT - See Regression Analysis
LINEAR REGRESSION - See Regression Analysis ...
Least Squares Moving Average
The Least Squares Moving Average first calculates a least squares regression line over the preceding time periods, then projects it forward to the current period.
A statistical method to derive the formula for a line that fits the data points by minimizing the sum of the squares of the deviations of the given points from the line. This method is used in calculating linear regression.
Sum Of Least Squares
The Sum Of Least Squares method provides an objective measure for comparing a number of straight lines to find the one that best fits the selected data.
LSMA - Moving Average
Also called "moving linear regression" or "regression oscillator".
See also Least Squares MA, Simple MA, Triangular MA, Weighted MA, Welles MA, Variable MA, Volume Adjusted MA, Zero Lag Exponential MA, DEMA, TEMA and T3.
The continuous SDE can be discretized as follows
Indirect least squares, a statistical technique
Instrument landing system, a precise navigation system for aircraft, used under instrument flight rules
Integrated library system, an enterprise resource planning system for libraries ...
Method: A technique of fitting a curve close to some given points that minimizes the sum of the squares of the deviations of the given points from the curve.
Leg: One side of a spread.
This item is calculated by using a least squares regression fit over a 3-to-5 year period of earnings per share based on a trailing four-quarter count.
TSF, the Time Series Forecast indicator, consists of linear regression measurements using the method. Linear regression is a statistical tool for forecasting future Forex market values comparing to past values.
Linear Regression is a tool used to measure trends and consists of a straight line drawn through the prices using the least squares method to plot the line.
The Time Series Forecast is determined by calculating a linear regression trendline using the " fit" method.
A Linear Regression trend line is simply a trend line drawn between two points using the least squares fit method. The trend line is displayed in the exact middle of the prices.
LSQ line - LSQ stands for a mathematical formula called "." In technical analysis, an LSQ line is a trend line that determines the midpoint of price data on a stock graph.
To calculate the Time Series Forecast you have to use a "least squares fit" technique to calculate a linear regression trendline, ...
The Classic Standard Regression Channel is like the Breakout Standard Regression Channel except the fit and the channel lines are computed using all of the data in the current plot.
Box-Jenkins Nonlinear Least Squares The multiplicative structure of Box-Jenkins models using the Gauss-Newton algorithm with numerical derivatives.
Linear Regression Channel is built on base of Linear Regression Trend representing a ussual trendline drawn between two points on a price chart using the method of .
EPS Growth Rate, 3-5 Year
This item is calculated by using a least squares regression fit over a 3-to-5 year period of earnings per share based on a trailing four-quarter count. more...
A Linear Regression Trendline is a straight line drawn through a chart of a security's prices using the method.
It may be estimated using ordinary least squares (OLS) regression analysis. Often an attribute vector (or dummy variable) is assigned to each characteristic or group of characteristics.
For example, an ANN that is used to fit a nonlinear regression curve, using one input, one linear output, and one hidden layer with a logistic transfer function, can function like a polynomial regression or spline.
Time Series Forecast
The Time Series Forecast is used to predict price movements. It consists of linear regression mesurements using the “Least Squares Method.' ...
See also: Square, Regression, Trend, Analysis, Linear Regression