Примери за използване на Regression equation на Английски и техните преводи на Български
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The regression equation takes the form.
Selection of an appropriate logistic regression equation;
The regression equation takes this form.
Thus, grouped information helps to find a regression equation.
Regression equation and regression line.
The multivariate regression equation has the form.
Use Excel to estimate the coefficients of the regression equation.
In other words, the regression equation takes the form.
The regression equation is performed at regular intervals, at least once daily, so that account can be taken of possible changes in column performance.
Because F count>F table, the regression equation is stated to be very significant.
The regression equation can take on a wide variety of values.
Optional text for a trendline,including either the regression equation or the R-squared value, or both.
The nonlinear regression equation, as well as the linear one, must be supplemented with a correlation index(R).
Each parameter that changes the value of the regression equation can be expressed through the equation. .
The linear regression equation is used in statistics to clearly explain the parameters of the equation. .
At the other extreme,if the coefficient of determination is 0, the regression equation is not helpful in predicting a y-value.
The multiple regression equation estimated using this indicator.
You can conclude, either by finding the critical level of F in a table orby using the FDIST function, that the regression equation is useful in predicting the assessed value of office buildings in this area.
Multiple linear regression equations were compiled for prediction of the Range of diameter and height classes Adj.
The regression model is determined through the least squares method, which provides the best approximation of the result estimate,determined through the regression equation, to its factors.
The non-linear regression equation is a bit contradictory.
Thanks to this type of analysis can assess the degree of interaction between multiple characteristics, between characteristics and results obtained,as well as to simulate the regression equation describing the shape of the relationship.
One of them is the regression equation considered in this article.
Well, it's obvious your victim was working with regression equations… maxillary sinus volume, high cheek bone placement.
Multiple linear regression equations were compiled for prediction of the Range of diameter and height classes(Adj. R2=0.86 and 0.81 respectively).
In addition, it is necessary to have a good understanding of the industry concerned, in the first place,to formulate the right hypothesis when constructing the regression equation and to make the right choice as to the factors that are likely to have significantly influenced the variable of interest(and which should therefore be included in the analysis).
The parameters of the linear regression equation are necessary to identify the type of dependence,the function of the regression equation, and the evaluation of the indicators of the chosen formula for the relationship.
The main stages of modeling regression equation are described: sampling data modeling;
In order to calculate the linear regression equation describing the relationship between the factors and the result in MS Excel to apply the function"LINEST".
Correlation and regression analysis in statistics allows you to create a regression equation and determine correlation coefficients, demonstrating the relationship between living standards and development of the territory.