Sentence examples for model for y from inspiring English sources
The model for y is constructed as follows: Assume that Pr y = m|x) is a linear combination xβ m.
For example, a first-order autoregressive ("AR(1)") model for Y is a simple regression model in which the independent variable is just Y lagged by one period (LAG Y,1) in Statgraphics or Y_LAG1 in RegressIt).
We concluded that the model for y ∗ ̂ given by Equation (12) was satisfactory, and we could estimate the speedup using back transformation given by y ̂ = e y ∗ ̂.
Often the parameters are denoted there by AR(1), AR(2), …, and MA(1), MA(2), … etc.. To identify the appropriate ARIMA model for Y, you begin by determining the order of differencing (d) needing to stationarize the series and remove the gross features of seasonality, perhaps in conjunction with a variance-stabilizing transformation such as logging or deflating.
Consider the single-variable model for Y as shown in Figure 2a.
To do so, we calculate the difference in BIC scores for the ordinary single-variable model for Y and a model that assumes the observations of Y1,.., YN are IID (a single-variable model for Y with λ→∞).
Due to satisfactory statistical parameters, applied models for Y and PC represented good approximation of experimental results.
For settings in which parametric models for Y, M, and L are specified via linear regression, this can be formally examined.
The orders of polynomials were determined using a Wald test in univariate models for y and ψ s and were kept the same for "macro micro", "macro", "micro" and "simple" models.
Here, the ANOVA of the regression model for response Y 1 [percentage removal of Cr VI)] and response Y 2 (percentage removal of phenol) shows that the model is highly significant which is confirmed by the calculated F value for response Y 1 (79.53) and response Y 2 (50.11) and a very low probability value (P ≤ 0.0001) was obtained for both responses Y 1 and Y 2 (Cao et al. 2014).
A unified representation of the models is provided by f y; ω) = ωI{0} y) + (1 − ω f Y (y), where Y is the count variable, I{0} is the indicator function and ω is a constant, whose values, if in (0,1) render a hurdle model for f Y (0) = 0, a zero-inflated model for f Y (0) ≠ 0, while negative values of it render a zero-deflated model.
