Quote from HarryJkevin91 on July 9, 2025, 5:48 am

Several key assumptions must be met for accurate results before performing linear regression. These include linearity between variables, independence of errors, homoscedasticity (equal variance of errors), and normally distributed residuals. Violating these assumptions can lead to biased or unreliable estimates. Checking diagnostic plots and statistical tests helps validate these conditions. The technical nature of these checks leads some students to search Pay someone to take my statistics class for extra support. Sometimes it's very helpful for students. Understanding these assumptions is important for perfect regression analysis.