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Least Squares

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Least Squares
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.

LEAST SQUARES FIT - See Regression Analysis
LINEAR REGRESSION - See Regression Analysis ...

Least Squares Method
A technique of fitting a curve close to some given points to minimize the sum of the squares of the deviations of the given points from the curve.
One side of a spread.

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. In essence, it calculates what the value would be if the regression line continued.

Least Squares Method
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.
Lesser/Higher Degree
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~ remapping A global remapping implemented using the method of ~.
legal personality A legal concept under which corporations are treated as artificial people, with a similar capacity for legal rights and obligations.

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 - Least Squares 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.
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Ordinary least squares
The continuous SDE can be discretized as follows
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The line is called the regression line or least squares line, because it is determined such that the sum of the squared distances of all the data points from the line is the lowest possible.
If we have two variables X and Y), the regression line will have the following form:
X = a + b Y ...

' Box-Jenkins Linear Least Squares The additive structure of Box-Jenkins models with a polynomial structure. Box-Jenkins Method From G.E.P. Box and G.M. Jenkins, who authored Time Series Analysis: Forecasting and Control.

For cases other than fitting by ordinary least squares, the R2 statistic can be calculated as above and may still be a useful measure. However, the conclusion that that R-squared increases with extra variables no longer holds, but downward variations are usually small.

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 Least Squares method. Linear regression is a statistical tool for forecasting future Forex market values comparing to past values. TSF tries to forecast the following Forex market value.

The Linear Regression Slope study displays expected price change based on linear regression analysis using the least squares method. High positive values of the slope might indicate a buying opportunity, while low negative values can be considered a signal to sell or open a short position.

The Time Series Forecast is determined by calculating a linear regression trendline using the "least squares fit" method. The least squares fit technique fits a trendline to the data in the chart by minimizing the distance between the data points and the linear regression trendline.

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.

To calculate the Time Series Forecast you have to use a "least squares fit" technique to calculate a linear regression trendline, which attempts to fit a trendline to the price data by minimizing the distance between the price points and the linear regression trendline itself.
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LSQ line - LSQ stands for a mathematical formula called "least squares." In technical analysis, an LSQ line is a trend line that determines the midpoint of price data on a stock graph.

The Classic Standard Regression Channel is like the Breakout Standard Regression Channel except the least squares fit and the channel lines are computed using all of the data in the current plot.

A Linear Regression Trendline is a straight line drawn through a chart of a security's prices using the least squares method.
The bottom channel line indicates support and the top channel line indicates resistance. Prices extending outside of the channel may suggest a trend reversal.

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 least squares. As a result, this line proves to be the exact median line of the changing price.

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 least squares spline. It has some advantages over the competing methods.

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: See also: What is the meaning of Square, Regression, Trend, Analysis, Linear Regression?

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