Least Square Method

◆ The "least square method" is a mathematical method to find the mathematical relationship of relevant data. It is widely used in data processing in many disciplines.

◆ in 1801, Gauss invented the "least square method" and calculated the orbit of Ceres. French scientist Legendre also invented the "least square method" in 1806.

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New Square Method

◆ The "New square method" is an improved approach based on the “least square method”. It calculates not only the constants and coefficients but also the variables’ power values in a model in the course of data regression calculations, thus bringing about a simpler and more accurate calculation for non-linear data regression processes.

◆ The "New square method" is a nonlinear regression method we developed due to the deficiency of our directed towards "least square method".

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Method Comparison

◆ The "least square method" is a special case of the "new square method". The "new square method" calculates the power value (any real number) k of the dependent variable. The "least square method" defaults the power value k of the dependent variable to 1

◆ The "new square method" uses the calculation of power value to make the regression model function curve bend with different curvature to better fit the data with different curvature and better fit the regression model with the data.

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Regression analysis of serial data

◆ Several data arranged in order, such as time series data, etc.

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Regression analysis of 2D data

◆ During data collection, each variable (x) is collected, and a dependent variable is collected accordingly.

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Regression analysis of 3D data

◆ During data collection, every time two different variables (X1 and x2) are collected, a dependent variable (y) is collected correspondingly.

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Regression analysis of 4D data

◆ During data collection, a dependent variable (y) is collected for every three different variables (x1, X2 and x3).

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Multidimensional linear regression analysis

◆ During data collection, every time n different variables (X1 , x2 , ...... xn ) are collected, dependent variable (y) is collected correspondingl y . x1, X2,... Xn and Y. are linear relations respectively

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Polynomial regression analysis

◆ It is an algorithm for regression of univariate nonlinear data, and the regression accuracy is not as accurate as "New square method" .

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Data preprocessing

◆ In the "new square method" regression, the power value of the variable should be calculated, so the variable cannot be < = 0, otherwise there will be an imaginary number. Therefore, the dimension of < = 0 dimension "+" a number or "x" a negative number.

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Variable and dimension setting

◆ When two-dimensional or larger data are regressed, determine the corresponding settings of data dimension and model variables in the model.

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Regression actuarial

◆ During "New square method" regression analysis and calculation, the selection of model is related to the initial value setting of power.

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Language: Simplified Chinese

Size: 37.9 MB

Description:Big data analysis system

(BDA) is a collection version of

quality control chart (QCC), data

regression analysis (DRS) and system

process analysis(SPA).



Language: Simplified Chinese

Size: 27.3 MB

Description:Quality control chart

(QCC) is a quality control tool

prepared according to the

national standard of the people's

Republic of China "GB_T_4091-2001".



Language: Simplified Chinese

Size: 34.1 MB

Description:Data regression analysis

(DRS) was prepared according to

"New Square Method". It makes the

regression calculation of one-

dimensional and multi-dimensional

linear and nonlinear data simpler

and the result more accurate.



Language: Simplified Chinese

Size: 36.8 MB

Description:System process analysis

(SPA) is a tool to analyze the data

of various relationships within a

system, establish mathematical

relationships and calculate. It

is widely used in practice.