Significance of Multiple linear regression
Multiple linear regression is a statistical method utilized to investigate the relationships between multiple independent variables and one dependent variable. It is employed in various studies to identify significant predictors related to factors such as weekly physical activity, caregiver burden, and diabetes-related deaths. This analysis aids researchers in understanding the impact of different variables, including sociodemographic and clinical factors, allowing for informed conclusions about correlations and influences in health-related contexts. It facilitates the examination of complex interrelationships among variables.
Synonyms: Multiple regression, Linear regression analysis, Multivariate linear regression
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The concept of Multiple linear regression in scientific sources
Multiple linear regression is a statistical method that analyzes relationships between multiple independent variables and a dependent variable, identifying significant predictors and understanding how various factors influence outcomes in diverse fields, including health and social studies.
(1) This is a statistical technique used to identify factors that are significantly related to the level of stress among the workers in the study.[1] (2) This is a statistical method used to analyze the relationship between multiple factors and an outcome, and can be used to determine interactions between ethnicity and co-factors.[2] (3) This analysis was applied to determine the predictors of perceived stress, and the sample size was calculated for this test.[3] (4) This is a statistical method used to identify the factors that are associated with the amount of weekly physical activity.[4] (5) Multiple linear regression is a statistical method used to analyze the relationship between a dependent variable and multiple independent variables, helping to understand how these factors predict an outcome.[5]
(1) This is the data analysis method used in the study to determine the relationship between blood glucose levels and systolic blood pressure with the length of stay of the patients.[6] (2) This was used to identify the significant predictors of knowledge and practice, revealing the factors most strongly associated with these aspects of the study.[7] (3) Multiple linear regression is a statistical method used to examine the relationship between multiple contextual factors and diabetes-related deaths, allowing researchers to identify significant predictors of mortality.[8] (4) Multiple linear regression analysis was used to determine the predictors of knowledge and PPE compliance, while Pearson Chi-square was employed to test the association between perceived health problems and other variables.[9] (5) This is a statistical method used to determine how several factors can predict changes in myocardial oxygen consumption, which is used in the study's analysis.[10]
(1) This is a statistical technique used to model the relationship between a dependent variable and two or more independent variables, allowing for the prediction of outcomes.[11] (2) This is a statistical method used to identify factors associated with better practices on sickle cell disease, separately for nurses and physicians, as part of the analysis.[12] (3) This is a statistical technique used to assess the relationship between a dependent variable and multiple independent variables, such as WHO stage, CD4 count, and psychomotor speed.[13] (4) This is a statistical method used to assess the impact of various factors on self-management practices, such as knowledge, attitudes, and other variables, to find correlations.[14] (5) This was performed to determine the independent predictors of health workers' knowledge on Alzheimer's disease, adjusting for age and sex in the analysis.[15]
(1) This is a statistical technique used to model the relationship between a dependent variable and two or more independent variables, aiming to determine the influence of each predictor.[16] (2) This statistical method was used to investigate the determinants of community reintegration, using socio-demographic and clinical variables as predictor variables.[17] (3) This model is used to establish the predictors of pulmonary function and analyze the relationship between multiple independent variables and a dependent variable.[18] (4) This is the statistical method employed to determine the differences in knowledge and attitudes between third- and fourth-year physiotherapy students, providing insights into the relationship between these factors.[19] (5) This is a statistical method used to determine the association between variables, such as sociodemographic and stroke-related clinical factors, and caregiver burden strain in the study.[20]
(1) A statistical technique used to analyze the relationship between a dependent variable and two or more independent variables.[21] (2) Multiple linear regression is a statistical technique used to model the relationship between a dependent variable and two or more independent variables, allowing for adjustment of confounding factors.[22]
(1) Models were used to assess the relationships between the independent variable and the dependent variables, adjusted for multiple factors.[23]
(1) This is a statistical method used to assess how multiple factors affect the total caregiver burden score.[24] (2) This statistical technique was used to assess how multiple variables influenced the outcomes, such as the impact of mental disorders on behaviors.[25] (3) The statistical method used to investigate the contribution of premorbid adjustment at different socio-sexual stages to the clinical variables, such as the severity of psychosis and global functioning.[26]
(1) A statistical technique used to assess the relationship between a dependent variable and multiple independent variables.[27]