In this paper
regression analysis is used to analyze cut perpendicularity with respect to cutting parameters: laser power, cutting speed and assist gas pressure during fiber laser cutting with [N.sub.2] as assist gas.
First is a multiple
regression analysis as a linear
regression analysis, and the second is a logistic-regression analysis as a nonlinear
regression analysis.
In this paper we use linear
regression analysis to show two paradoxes in
regression analysis.
Miu and Risa, two young tea-shop workers, are quickly introduced before Risa agrees to help her friend understand
regression analysis. Together, they calculate the expected sales of tea depending on the high temperature of the day and, later, create sales projections for a proposed new bakery location.
In the
Regression analysis A for Sample 1, the indicators of working capital management and liquidity of sample 1 are regressed against the 'Return on Assets'.
Regression analysis. Modeling yarn characteristics is an important field of the textile spinning research.
With the mathematics of
regression analysis, those adjustments can be accurate every time.
Regression Analysis.
Regression analysis is used to fit the curve of the relationship between the input and output database.
Fuzzy regression model can be simplified to interval
regression analysis which is considered as the simplest version of possibilistic
regression analysis with interval coefficients.
As a result, the present results confirmed that instead of multiple and stepwise regression, principal use of factor and principal component scores in multiple
regression analysis might offer a good opportunity without multicollinearity problem for predicting body weight of indigenous goat.
Multiple linear
regression analysis was used to determine the extent to which changes in participants' attitudes, subjective norms, and perceived behavioral control predicted their intentions to advocate for school health education.
In the factor analysis, three new latent variables whose eigenvalues were greater than one were considered as explanatory variable for multiple linear
regression analysis. Results obviously illustrated that 79.1% of variation in body weight was effectively explained by these new latent explanatory variables.
