Examples of using Linear regression in English and their translations into Croatian
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Linear Regression.
Genus Corp just switched to linear regression accounting.
Linear Regression- trend indicator.
Forecast will be produced using the folloeing linear regression formula: Ct*=.
Your linear regression?
Configure at least basic indicators- Stoch,RSI, linear regression indicator.
Linear regression has many practical uses.
Something, discrete andcontinuous random variables, and simple linear regression.
Linear Regression(Regression coefficient, regression equation).
The known values are existing x-values and y-values, andthe new value is predicted by using linear regression.
Linear Regression can be applied to cross-sectional data or as it is in or case time series data.
First, Einav andcolleagues made these estimates separately for items of different prices and without using linear regression.
Apply linear regression model and conduct statistical test related to the linear regression, with or without a computer.
Many researchers estimate the heterogeneity of treatment effects using linear regression, but newer methods rely on machine learning;
Multiple linear regression revealed a linear influence of all these active variables on 5-FU and INDO flux.
Keywords: The extended tool life equation, Quenched and tempered steel 42 CrMo 4( DIN), Mathematical model,Multiple linear regression analysis, Turning.
Linear Regression indicator- is used for trend identification and trend following in a similar fashion to moving averages.
The results suggest the possibility of application of the linear regression method in the drag estimation for periods for which microbarographic data are not available.
With only continuous predictor variables in SPSS GLM, which defines the predictor variable as continuous.by putting them in a co-variant box You can run a linear regression model.
I-Regr: Linear Regression Channel consists of two parallel lines,equidistant up and down from the line of linear regression trend.
With only continuous predictor variables in SPSS GLM,by putting them in a co-variant box You can run a linear regression model which defines the predictor variable as continuous.
You can run a linear regression model with only continuous predictor variables in SPSS GLM, by putting them in a co-variant box which defines the predictor variable as continuous.
Presuming a linear dependance between the reference element and andthe heavy metal it is possible with the use of linear regression analysis to simultaneously define the heavy metal backgroun and to isolate natural and/ or anthropogenic outliersanomalies.
You can run a linear regression model which defines the predictor variable as continuous. with only continuous predictor variables in SPSS GLM, by putting them in a co-variant box.
Even if you are not particularly interested in auctions on eBay, you have to admire the way that Figure 2.7 andFigure 2.8 offer a richer understanding of eBay than simple linear regression estimates that assume linear relationships and combine many different categories of items.
First, using linear regression they estimated that higher starting prices decrease the probability of a sale, and that higher starting prices increase the final sale price, conditional on a sale occurring.
Many researchers estimate the heterogeneity of treatment effects using linear regression, but newer methods rely on machine learning, for example Green and Kern(2012), Imai and Ratkovic(2013), Taddy et al.
Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the"lack of fit" in some other norm(as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression(L2-norm penalty) and lasso L1-norm penalty.
Various statistical methods( principal component analysis, cluster analysis,single and multiple linear regression) were applied to study a quantitative relationship between structural and quantum-chemical features with soil sorption characteristics of agricultural chemicals.
Experimental results were treated with multiple linear regression analysis and confirmed with equation of mathematical model, which shows dependency of tool life on cutting speed, feed and depth of cut.