Sentence examples for linear regression estimation from inspiring English sources

The study contrasts two types of models that have been employed to estimate project duration and effort separately: linear regression estimation models and models deriving from a more novel approach based on artificial neural networks (ANNs).

Table 3 and Fig. 4 shows the estimates of missing values by linear spline interpolation and linear regression estimation methods.

The topics in the course include basic probability and statistics; linear regression; estimation and testing of economic model.

1. Vectors of Random Variables -- 2. Multivariate Normal Distribution -- 3. Linear Regression: Estimation and Distribution Theory -- 4. Hypothesis Testing -- 5. Confidence Intervals and Regions -- 6. Straight-Line Regression -- 7. Polynomial Regression -- 8. Analysis of Variance -- 9. Departures from Underlying Assumptions -- 10. Departures from Assumptions: Diagnosis and Remedies -- 11.

The framed hypotheses are tested using multivariate regression with ordinary least square, i.e., linear regression estimation.

For linear regression, estimation involves minimisation of ( sum limits_{mathrm{i}=1}^{mathrm{n}}mathrm{Q}left({mathrm{r}}_{mathrm{i}}right) ), with corresponding estimation equations ( frac{1}{mathrm{n}}sum limits_{mathrm{i}=1}^{mathrm{n}}{mathrm{X}}_{mathrm{i}mathrm{j}}uppsi left({mathrm{r}}_{mathrm{i}}right)=0 ), where ψ(r) = ∂Q(r)/∂r is the score or influence function.

The solution was able to provide a good non-linear regression estimation along with adaptation capabilities to changing conditions.

SVR is a novel learning methodology based upon statistical learning theory which is applicable for solving the complex non-linear regression estimation problems (Asoodeh et al. 2014a, b, c; Bagheripour et al. 2015).

We used ordinary least-squares linear regression for estimation of the coefficients.

By multiplying the probability of the logistic regression with the result of the linear regression, an estimation of the absence days is obtained.

In the context of linear regression, an estimation based on an instrumental variables approach may be considered, when the endogeneity of regressors is suspected [ 18].