Regression Analysis
A statistical method for modeling the relationship between an outcome variable and one or more predictors, estimating how the outcome changes with each.
Regression analysis models how a dependent variable changes as one or more independent variables change. Simple linear regression fits a straight line to one predictor; multiple regression includes several predictors at once, estimating each one's association with the outcome while holding the others constant. Output typically includes coefficients, their significance tests, and a measure of how much variance the model explains.
Variants extend the idea: logistic regression handles binary outcomes, and other forms handle counts, ordered categories, or change over time.
For thesis writers, regression is often the main analysis because it can test hypotheses while statistically controlling for confounding variables. Justify your choice of predictors from the literature, check assumptions such as linearity and the behavior of residuals, and report coefficients with confidence intervals so examiners can see both direction and precision.
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