Prediction of Egg Weight from External Egg Traits of Guinea Fowl Using Multiple Linear Regression and Regression Tree Methods

The study was done to predict egg weight from the external traits of the Guinea fowl egg using the statistical methods of multiple linear regression (MLR) and regression tree analysis (RTA). A total of 110 eggs from a flock of 23-week-old Guinea fowl were evaluated. Egg weight (EW) and external traits: eggshell weight (ESW), egg polar diameter (EPD), egg equatorial diameter (EED), egg shape index (ESI), and egg surface area (ESA) were measured. Descriptive statistics, Pearson correlation coefficients, and regression equations using the MLR were obtained; additionally, a RTA was done using the CHAID algorithm with the SPSS software (IBM ver. 22). EW presented positive correlations (p<0.0001) with ESA (r = 0.72), EPD (r = 0.65), and EED (r = 0.49). EW can be predicted through MLR using ESA as a predictor variable (R2 = 72%). Predictive accuracy improves when adding EPD and EED traits to the model (R2 = 75%). The RTA built a diagram using ESA, EED, and EPD as significant independent variables; of these, the most important variable was ESA (F = 50,295, df1 = 4, and df2 = 105; Adj. p<0.000) and the variation explained for EW was 74%. Likewise, the RTA showed that the highest egg weight (41.818 g) is obtained from eggs with a surface area > 59.03 cm2 and a polar diameter > 5.10 cm. The proposed statistical methods can be used to reliably predict the egg weight of Guinea fowl.(AU)

Prediction of Egg Weight from External Egg Traits of Guinea Fowl Using Multiple Linear Regression and Regression Tree Methods

The study was done to predict egg weight from the external traits of the Guinea fowl egg using the statistical methods of multiple linear regression (MLR) and regression tree analysis (RTA). A total of 110 eggs from a flock of 23-week-old Guinea fowl were evaluated. Egg weight (EW) and external traits: eggshell weight (ESW), egg polar diameter (EPD), egg equatorial diameter (EED), egg shape index (ESI), and egg surface area (ESA) were measured. Descriptive statistics, Pearson correlation coefficients, and regression equations using the MLR were obtained; additionally, a RTA was done using the CHAID algorithm with the SPSS software (IBM ver. 22). EW presented positive correlations (p 59.03 cm2 and a polar diameter > 5.10 cm. The proposed statistical methods can be used to reliably predict the egg weight of Guinea fowl.

Rapid Automatized Naming and Explicit Phonological Processing in Children With Developmental Dyslexia: A Study With Portuguese-Speaking Children in Brazil.

Many studies have shown that children with reading difficulties present deficits in rapid automatized naming (RAN) and phonological awareness skills. The aim of this study was to examine RAN and explicit phonological processing in Brazilian Portuguese-speaking children with developmental dyslexia and to explore the ability of RAN to discriminate between children with and without dyslexia. Participants were 30 children with a clinical diagnosis of dyslexia established by the Brazilian Dyslexia Association and 30 children with typical development. Children were aged between 7 and 12, and groups were matched for chronological age and sex. They completed a battery of tests that are commonly used in Brazil for diagnosing dyslexia, consisting of the Wechsler Intelligence Test for Children (WISC-IV) as well as tests of single word and non-word reading, RAN, and the profile of phonological abilities test. Results indicate that the cognitive profile of this group of children, with a clinical diagnosis of dyslexia, showed preserved skills in the four subscales of the WISC-IV (verbal comprehension, perceptual reasoning, working memory, and processing speed) and on the profile of phonological abilities test. Groups significantly differed on the reading tests (word and non-word) and RAN measures, with medium to large effect sizes for RAN. Classification and regression tree analysis revealed that RAN was a good predictor for dyslexia diagnosis, with an overall classification accuracy rate of 88.33%.

Prediction of large esophageal varices in cirrhotic patients using classification and regression tree analysis

OBJECTIVES: Recent guidelines recommend that all cirrhotic patients should undergo endoscopic screening for esophageal varices. That identifying cirrhotic patients with esophageal varices by noninvasive predictors would allow for the restriction of the performance of endoscopy to patients with a high risk of having varices. This study aimed to develop a decision model based on classification and regression tree analysis for the prediction of large esophageal varices in cirrhotic patients. METHODS: 309 cirrhotic patients (training sample, 187 patients; test sample 122 patients) were included. Within the training sample, the classification and regression tree analysis was used to identify predictors and prediction model of large esophageal varices. The prediction model was then further evaluated in the test sample and different Child-Pugh classes. RESULTS: The prevalence of large esophageal varices in cirrhotic patients was 50.8 percent. A tree model that was consisted of spleen width, portal vein diameter and prothrombin time was developed by classification and regression tree analysis achieved a diagnostic accuracy of 84 percent for prediction of large esophageal varices. When reconstructed into two groups, the rate of varices was 83.2 percent for high-risk group and 15.2 percent for low-risk group. Accuracy of the tree model was maintained in the test sample and different Child-Pugh classes. CONCLUSIONS: A decision tree model that consists of spleen width, portal vein diameter and prothrombin time may be useful for prediction of large esophageal varices in cirrhotic patients.