Table 4: Testing of Multiple Linear

Regression Model ANOVA

A statistical approach that examines the existence and quantify any possible associations between Y, and the presence of a set of factors X that exert influence on their behavior, is the Beta

Regression Model.

Logistic regression analysis has similarity to a linear

regression model but is appropriate in situations where there exist binary outcomes of dependent variable.

In these figures, x-axis is the difference in percentage between

regression model and tests in terms of either fuel mass or BSFC.

Estimation of genetic parameters for testday milk yield in Holstein cows using a random

regression model.

In order to model these two variables obtained from blood transfusion data, the bivariate zero-inflated Poisson

regression model was used.

In this regard, we compare the results of three models; Threshold

regression model, smooth transition

regression model and Markov regime-switching model.

He applied the

regression model to medical claims for ICD-9-CM-diagnosed pertussis in individuals under age 50 in the IMS PharmMetric Plus claims database for the years 2008-2013.

However tobacco use was not found to be statistically significant in the final multiple logistic

regression model.

When breastfeeding and infant growth were entered into the

regression model and adjusted for covariates, breastfeeding was no longer statistically significantly associated with BMI, while early growth remained statistically significantly associated with BMI.

Linear regression is arguably the most popular

regression model in practice, because of the ubiquity of continuous outcomes and because it is relatively easy to understand the modeled relationship and interpret the model estimates.

Consequently, researchers specifying

regression models must identify a valid model, decide whether to weight the

regression model, and determine how to best estimate the associated standard errors.

Ureyen and Kadoglu (2007), used a linear

regression model to predict the cotton yarn properties on the basis of fibre properties.

The aim of this study was to compare two different lactation curve models (Woodand cubic spline

regression model in two knots: CSR1 and CSR2) and to find the best model that provided a good description of the first lactation curve of Jersey cattle herd.

The

regression model with univariate skew-normal error had a high positive value for the asymmetric parameter ([lambda]) meaning a positive asymmetry (Table 1).