Significance of Regression analysis
Regression analysis is a statistical method widely used across diverse fields like science, psychiatry, health sciences, religion, and environmental science. It determines relationships between variables, identifies predictors, and assesses impacts. Applications range from optimizing medium composition and assessing mental health outcomes to modeling mortality rates, predicting cranial capacity, exploring factors influencing nursing workload, and evaluating green finance impacts. This versatile technique aids in understanding complex relationships and predicting outcomes in various research contexts.
Synonyms: Statistical modeling, Predictive modeling, Data analysis, Trend analysis, Correlation analysis, Regression modeling, Statistical regression, Statistical analysis
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Regression analysis in scientific sources
Regression analysis, a statistical method, models relationships between variables. The technique is used for prediction, examining correlations, and controlling for confounders across diverse fields like medicine, environmental science, and construction.
From: Sustainability Journal (MDPI)
(1) Regression analysis is a traditional statistical method, but data mining techniques are presented as superior in terms of rigor and accuracy for detecting financial statements fraud.[1] (2) Regression analysis is a statistical method used to obtain the confinement coefficient (K s) based on experimental data and influencing parameters.[2] (3) Regression analysis was one of the statistical methods used to test hypotheses and examine the relationships between variables, particularly the impact of online learning platforms on academic performance.[3] (4) Regression analysis is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables, applied here to understand land use drivers.[4] (5) Regression analysis was used in the post hoc analysis to assess the statistical significance and influence of different configurations on high synergetic development.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Regression analysis was performed to investigate the relationship between the tendency to use separate spaces for activities and AHN proxy measures, finding no significant results.[6] (2) Regression analysis is a statistical method used to determine the relationship between a dependent variable and one or more independent variables, and it was employed to predict flight attendants' COVID-19-preventive behaviors.[7] (3) Regression analysis is employed to predict phenomena based on variable measurements, aiming to formulate an equation for calculating the OCRA Index using skin temperature variation of analyzed regions.[8] (4) Regression analysis was employed to examine the relationships between maternal distress, filial responsibilities, and adolescent mental health outcomes.[9] (5) A statistical method used to estimate the relationships between a dependent variable and one or more independent variables.[10]
From: Asian Journal of Pharmaceutics
(1) Outcome of linearity was utilized in this by means of least squares technique.[11] (2) A statistical method used to derive model predictor equations for each dependent variable.[12] (3) The regression analysis indicates the linearity between the concentrations of the analyte with the area under the peak.[13] (4) The linearity of the method was determined using 1/x 2 weighted least square regression analysis of standard plots associated with a 10-point standard curve.[14] (5) A statistical method used to model observational data and determine the relationship between variables, including dose-response curves.[15]
From: The Malaysian Journal of Medical Sciences
(1) This was adopted to compute the prediction norms for predicting pulmonary function measurements from different physical parameters in the study.[16] (2) A linear version was run to determine the factors associated with the total postnatal care cost, and the education level, type of primary care facilities, and the number of postnatal visits were considered in the model.[17] (3) Regression analysis is a statistical method used to derive age-, gender-, and population-specific models, which are used to predict cranial capacity based on other measured parameters.[18] (4) This statistical method was used to identify significant predictors of cognitive impairment, with depression emerging as the only significant risk factor in the multivariate analysis.[19] (5) This method was employed to investigate the predictive role of perceived social support and spiritual intelligence in the patients’ hope.[20]
From: African Journal of Primary Health Care and Family Medicine
(1) The text mentions that, in the regression analysis, three contraceptive methods and age were found to influence discontinuation, highlighting the importance of these factors in contraceptive use.[21] (2) This is a type of statistical analysis used to assess the association between the diabetic foot risk category and various factors, as described in the study.[22] (3) This is a statistical process for estimating the relationships among variables, and it was used to determine how various factors influenced the TMI and BMI of the children.[23] (4) The process used to identify predictors of patient safety attitude, where the highest degree of education and job type were significant predictors among the healthcare workers under study.[24] (5) Regression analysis is used to determine whether one continuous variable has a significant influence over or correlation with another continuous variable, as described in the text.[25]
From: Journal of Public Health in Africa
(1) This section presents the parameters of the regression model estimated using the OLS method to quantify the impact of the COVID-19 pandemic on antenatal indicators.[26] (2) Finally, the multivariate of this was performed to assess the relationships between knowledge about hepatitis B in children among health practitioners and other variables at P<0.05 and 95% confidence interval.[27] (3) This is a statistical method, discussed in the provided text, that is used for analyzing and understanding data, and is the subject of information provided by Statistics Solutions.[28] (4) A statistical process for estimating the relationships among variables, used in the study.[29] (5) Regression analysis is performed to identify predictors of women’s mental health immediately after delivery, considering potential predictors from previous univariate statistical analyses.[30]
From: South African Journal of Physiotherapy
(1) Regression analysis statistics were used to determine the predictors of postoperative outcomes, identifying preoperative vigorous-intensity physical activity and functional performance status as significant factors.[31] (2) Regression analysis is a statistical process for estimating the relationships among variables, and regression analysis indicated that a low BI score was the only factor found to predict mortality.[32] (3) It revealed a slower rate of postural reaction development in infants with Down Syndrome at an older age.[33] (4) This is a statistical method used to determine the factors that predict the distance walked during the 6-minute walk test.[34] (5) This statistical method was used to analyze the relationship between estimated and actual values of 1-RM and 10-RM, using a prediction model with an intercept for the analysis.[35]
From: International Journal of Pharmacology
(1) The MIC50 and LC50 values were determined from the best-fit line obtained by this analysis of the percentage hatchability and lethality versus the concentration.[36] (2) The regression analysis in the RSM helps to evaluate the effective factors and to study the interaction between these factors.[37] (3) It was applied using each one of the factors as an independent variable.[38] (4) Peak area and concentration of each drug substance was subjected to this to calculate the regression equation.[39] (5) This showed non-significant relationships between urine calprotectin and FBS, PPBS or HbA1c in all groups.[40]
From: South African Family Practice
(1) Regression analysis is an economic tool used to estimate workforce needs, suggesting Australia will require a significant number of new GPs annually.[41] (2) This is a statistical method used to analyze the relationship between variables, such as home delivery of medication and HbA1c levels, to determine their association and impact on type 2 diabetes control.[42] (3) A statistical method used to identify factors that are associated with quit attempts.[43] (4) This is a statistical method used to examine the relationship between variables, such as profession, qualifications, and experience, and their impact on PMTCT.[44]
From: South African Journal of HIV Medicine
(1) It is a statistical method used to determine the relationship between a dependent variable and one or more independent variables, such as predictors of neurodevelopmental outcomes.[45] (2) This is a statistical method used to examine the relationship between a dependent variable and one or more independent variables, often to determine mediation.[46] (3) This is a statistical method used to examine the relationship between the medication exposure, bone markers, and bone mineral density.[47] (4) This is a statistical method used to test the relationship between smoking and pulmonary function in people living with HIV, with multivariable analyses performed to assess the relationships.[48]
From: Onderstepoort Journal of Veterinary Research
(1) A statistical method used to determine the relationship between fish size, parasite numbers, and condition factor.[49] (2) A statistical technique utilized to analyze data and determine relationships between variables, providing insights into the accuracy and precision of the selenium analysis process.[50] (3) Regression analysis is a statistical technique used to examine the relationship between variables, and it was used in the study to analyze quantitative data and its associations.[51]
From: South African Journal of Psychiatry
(1) Hierarchical multiple regression analysis was used to test the health-sustaining function of psychosocial factors in rheumatoid arthritis patients.[52] (2) It is mentioned in Sampling weights and regression analysis. Sociol Methods Res 1994;23:230-257.[53] (3) A statistical method employed to determine the associations between substance use and duration of untreated psychosis, considering various factors.[54] (4) The text uses this term to describe the statistical method used to assess the combined association between variables and depressive symptomology in the study.[55] (5) This was done with GHQ-30 scores as the dependent variable and sleep duration and ESS as the independent variables, to determine the relationships between these variables.[56]
From: Religions Journal (MDPI)
(1) This is a statistical method used to examine the relationship between a dependent variable and one or more independent variables, often employed in testing causal models.[57] (2) This technique explores the connection between a dependent variable and one or more independent variables. It aims to model this relationship for prediction or understanding causality.[58] (3) It is a statistical technique used to model the relationship between a dependent variable and one or more independent variables, allowing for prediction and explanation.[59] (4) is a statistical technique used to model the relationship between a dependent variable and one or more independent variables, allowing researchers to predict and explain variations in the dependent variable.[60] (5) Is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables, assessing the strength and direction of their association.[61]
From: International Journal of Pharmacology
(1) This analysis was performed to optimize medium composition and represented by a regression model, confirming the significance and adequacy of the model.[62]