Significance of Linear regression analyses
Linear regression analyses, as defined by both Psychiatry and Health Sciences, are statistical methods used to explore relationships between variables. Psychiatry uses it to examine associations between factors like substance use and childhood experiences, as well as to understand relationships between neurocognition and brain volumes. Health Sciences employs it to model relationships between dependent and independent variables, such as job satisfaction and health outcomes, or to analyze trends in habituation tests.
Synonyms: Regression analysis, Linear modeling, Least squares analysis, Statistical regression, Predictive modeling, Least squares fitting, Trend analysis, Statistical modeling, Regression modeling.
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Linear regression analyses in scientific sources
Linear regression analyses are statistical tools used to investigate relationships between variables. These methods assess trends, determine associations, and model relationships, such as startle responses, job satisfaction, neurocognition, and caregiver burden, while also accounting for influencing factors.
From: International Journal of Environmental Research and Public Health (MDPI)
(1) It is a statistical method used to examine the associations between the physical home environmental factors and the objectively measured physical activity and sitting behaviors of youth.[1] (2) Linear regression analyses is a statistical method used to determine the effects of various factors on length of stay.[2] (3) It is a statistical method used to demonstrate that screen time variables were only significantly associated with body image score for girls, both cross-sectionally at ages 15 and 17, and longitudinally between time points.[3] (4) Linear regression analyses were performed to examine the association between maternal perception of child’s weight and weight development, using data from the Amsterdam Born Children and their Development birth-cohort study.[4] (5) It involves the use of somatic symptoms (PHQ-15) as dependent variables and estimates the associations between somatic symptoms and HADS-A and HADS-D scores.[5]
From: Sustainability Journal (MDPI)
(1) Linear regression analyses, along with Pearson correlation, were performed between the satellite-derived annual mean Chla and driving factors, to answer the question about the dominant driving factor.[6] (2) Linear regression analyses are statistical methods used to determine the relationship between variables, such as demographic factors and attitudes, and the frequency of bicycle use, identifying significant predictors of cycling behavior.[7] (3) Linear regression analyses are used to examine the relationships between the extent of wildfires and factors like temperature and precipitation, helping to explain the trends observed from 2002 to 2022.[8] (4) Several linear regression analyses were conducted to understand the interaction between the reward-system-experience values, the perceived behavioral outcome, and the fun level expressed by the questionnaire for each campaign.[9] (5) It is used to statistically evaluate data from the TiO2 experiments, assessing the significance of primary variables and interaction terms on % removal.[10]
From: The Malaysian Journal of Medical Sciences
(1) A statistical method used in the study to determine the relationship between job satisfaction and health outcomes.[11] (2) A statistical method used to model the relationship between a dependent variable and one or more independent variables.[12]
From: African Journal of Primary Health Care and Family Medicine
(1) These are statistical techniques used to model the relationship between a dependent variable and one or more independent variables, and they were employed in this research.[13] (2) These analyses were computed to further examine the relationships between the independent and dependent measures, corrected for gender, race, and locality.[14]
From: International Journal of Pharmacology
(1) These were used to determine the causative relationship between blood alcohol concentration and vitreous humor in the first and sixth month, as well as the relationship between initial concentrations and changes after six months.[15] (2) Linear regression analyses are statistical methods used to examine the relationship between variables, specifically applied here to assess the trend of startle response magnitudes over repeated trials in habituation tests.[16]
From: Asian Journal of Pharmaceutics
(1) These are statistical methods used to determine the relationship between two variables, such as drug concentration and peak area.[17] (2) This analysis of the mean percentage of dose absorbed versus the mean in vitro release resulted in a significant correlation.[18]
From: South African Journal of Psychiatry
(1) Linear regression analyses is a statistical method used in the study to examine the associations between various factors, such as substance use risk scores and adverse childhood experiences.[19] (2) Linear regression analyses are statistical methods used in the study to identify determinants or predictors of total caregiver burden, and they help to determine the relationship between variables.[20] (3) A statistical method used to determine the relationship between neurocognition and brain volumes in HIV-positive women with ELS.[21]