China
Africa
Explore chapters and articles related to this topic
Multiple Linear Regression
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
In the regression model that contains gestational age, preeclampsia, and the interaction between the two, preeclampsia and the gestational age–preeclampsia interaction are highly correlated. In fact, the Pearson correlation coefficient quantifying the linear relationship between these two variables is . This model and the model that did not include the interaction term are contrasted in Table 18.1. When the interaction term is included in the equation, the estimated coefficient of preeclampsia doubles in magnitude. In addition, its standard error increases by a factor of 12. In the model without the interaction term, the coefficient of preeclampsia is significantly different from 0 at the 0.05 level; when the interaction term is present, it no longer achieves statistical significance. The coefficient of determination does not change when the interaction is included. It remains 65.3%. Furthermore, the adjusted R2 decreases slightly. These facts taken together indicate that the inclusion of the gestational age – preeclampsia interaction term in the regression model does not explain any additional variability in the observed values of head circumference, beyond that which is explained by gestational age and preeclampsia alone. The information supplied by this term is redundant.
Intelligent Data Analysis Techniques
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Regression analysis has been used to model recovery, disease progression and medication dosage analysis. A regression is strongly correlated if the value of the dependent variable changes significantly when the independent variable changes. The correlation is measured using Pearson correlation-coefficient that varies between [− 1.0 … +1.0]. Positive coefficient shows positive correlation, and negative coefficient shows negative correlation. The choice of an independent variable causally connected to dependent variable is very important. In the presence of multilinear regression analysis, the correlation may not show up correctly due to the cumulative effect of the independent variables.
Investigating links between diet and health outcomes
Published in Geoffrey P. Webb, Nutrition, 2019
When correlating two variables (x and y) then a perfect positive correlation (increase in x leads increase y) yields a Pearson correlation coefficient (usually symbolised by the letter r) of +1 and perfect negative correlation (increase in x leads to decrease in y) yields an r value of −1. If you plot these two variables on a graph then you get perfect straight lines, sloping upwards (+1) or downwards (−1). If there is no relationship between x and y, the graph would yield a horizontal line and the correlation coefficient would be 0. Once again, it is highly unlikely that if you correlated even two completely unrelated variables, the correlation coefficient would be exactly zero or 1/−1 for associated variables; correlation coefficients almost always lie somewhere between 0 and +1 or −1.
2D-QSAR, 3D-QSAR, molecular docking and ADMET prediction studies of some novel 2-((1H-indol-3-yl)thio)-N-phenyl-acetamide derivatives as anti-influenza A virus
Published in Egyptian Journal of Basic and Applied Sciences, 2022
Mustapha Abdullahi, Adamu Uzairu, Gideon Adamu Shallangwa, Paul Andrew Mamza, Muhammad Tukur Ibrahim
In addition, the population sample was set to 10,000, the maximum generation was set to 1000, and the number of top equations was set to 1 for an effective model convergence [19]. The descriptor matrix of the built model was initially subjected to the Y-Randomization test as a measure to attest to the quality of the model before being exported to Molegro Data Modeler (MDM) for the development of the multi-linear regression (MLR) and the non-linear regression model version based on artificial neural network (ANN) analysis [18,20]. The predictive ability of the GFA-MLR and GFA-ANN models generated was examined using the following internal validation parameters as follows: The Pearson correlation coefficient (r): is a measure of the correlation of two variables x and y. It is mathematically defined as:
The relationship of chronotypes with food addiction, impulsivity, and attention deficit hyperactivity disorder symptoms in a sample of undergraduate university students
Published in Chronobiology International, 2022
Baris Yilbas, Halil Ibrahim Ozturk, Pınar Gunel Karadeniz
IBM SPSS Statistics for Windows, version 23 (IBM Corp., Armonk, NY) software package was used for the analysis of the study data. Descriptive statistics were expressed as mean and standard deviation (minimum-maximum values) for numerical variables and as frequency and percent values for categorical variables. Whether the numerical data showed a normal distribution was tested using Kolmogorov-Smirnov test. For group comparisons of the numerical data, independent-samples t-test was used to compare the means of two groups. Since the group variances were non-homogeneous, Welch’s ANOVA was used to compare the means of multiple groups. Games-Howell multiple comparison test was employed for comparisons with significant ANOVA results. Categorical variables were compared among the groups using chi-squared test or chi-square test continuity correction as appropriate. Pearson correlation coefficient was used in normally distributed data to determine the relationships between continuous variables. A p value less than 0.05 was considered significant for all analyses.
Self-practice among patients with psoriasis: University hospital experience
Published in Journal of Dermatological Treatment, 2022
Shaimaa Ismail Omar, Moustapha Ahmed Ramadan
The following tests were used:Chi-square test. This test was performed to compare unpaired categorical variables between different groups.Monte Carlo correction. Correction for chi-square test was performed when one or more of the test preconditions are not fulfilled.Pearson correlation coefficient. This test was conducted to correlate two normally distributed quantitative variables.F-test (ANOVA). This test was performed to compare normally distributed quantitative variables between more than two groups.