Regression Analysis Support
Regression analysis support for linear, logistic, generalized, multivariable, and research-specific modelling workflows.
Regression is not one test but a family of models used to estimate relationships while accounting for study design and covariates. Tezyar helps select a model appropriate to the outcome, specify predictors carefully, check assumptions, assess robustness, and report estimates with uncertainty.
Match the Model to the Outcome
Continuous, binary, count, ordinal, time-to-event, and repeated outcomes require different model families and assumptions.
Predictor and Covariate Strategy
Covariates should be chosen from the research question, design, causal reasoning, and prespecified analysis plan rather than automated p-value filtering alone.
Diagnostics and Sensitivity
Residuals, influential observations, collinearity, nonlinearity, interactions, clustering, and missing data can affect interpretation and may require alternative specifications.
Interpretation Beyond P-values
A useful report emphasizes effect size, direction, confidence intervals, practical meaning, and limits of inference.
Frequently Asked Questions
Which regression model do I need?
That depends primarily on the outcome type, study design, repeated or clustered structure, and research question.
Can you review a regression requested by a journal reviewer?
Yes. We can assess the comment, determine whether a revised model is justified, and align the response with the updated results.
Related specialized topics
SPSS analysis support for research projects, theses, and manuscripts: data screening, test selection, modelling, interpretation, and reporting.
R analysis support for reproducible research, statistical modelling, visualization, reporting, and thesis or manuscript workflows.
Structural equation modeling support for CFA, latent variables, path models, fit assessment, model revision, and reporting.
Sample size and power analysis support for research design, theses, grant proposals, experiments, surveys, and clinical studies.
Survey analysis support for data cleaning, scale reliability, descriptive analysis, group comparisons, regression, factor analysis, and reporting.
Statistical support for clinical and health research: analysis planning, outcomes, regression, survival methods, repeated measures, and reporting.