query: Does the model $E[Y]=\beta_0 + \beta_1 X_1 + \beta_2 X_1^2 + \beta_3 X_1^3$ meet the assumption of linearity for multiple linear regression?
positive_passages.docid: 17773
positive_passages.text: Why is polynomial regression considered a special case of multiple linear regression?
negative_passages.docid: 39548
negative_passages.text: Multicollinearity in OLS
negative_passages.docid: 91668
negative_passages.text: Sign of Estimate (Coefficient) of Interaction Terms
negative_passages.docid: 3401
negative_passages.text: The variance of linear regression estimator $\beta_1$
negative_passages.docid: 155765
negative_passages.text: How to derive the covariance matrix of $\hat\beta$ in linear regression?
negative_passages.docid: 9356
negative_passages.text: Derivation of the formula for Ordinary Least Squares Linear Regression
negative_passages.docid: 67090
negative_passages.text: Casting a multivariate linear model as a multiple regression
negative_passages.docid: 95315
negative_passages.text: Reference Request: Generalized Linear Models
negative_passages.docid: 58645
negative_passages.text: What does this plot tell me about my linear model?
negative_passages.docid: 7970
negative_passages.text: Why does the product of the bivariate regression coefficients of the $y$-on-$x$ line and $x$-on-$y$ line equal the square of the correlation?
negative_passages.docid: 19831
negative_passages.text: How to get the degree of freedom when doing multiple linear regression?
negative_passages.docid: 52796
negative_passages.text: Converting the beta coefficient from matrix to scalar notation in OLS regression
negative_passages.docid: 16308
negative_passages.text: Is the response linear in the predictor variable in the linear regression model?
negative_passages.docid: 69828
negative_passages.text: How do I use the standard regression assumptions to prove that $\hat{\sigma}^2$ is an unbiased estimator of $\sigma^2$?
negative_passages.docid: 107573
negative_passages.text: Does the slope of a regression between observed and predicted values always equal the $R^2$ of the original model?
negative_passages.docid: 130013
negative_passages.text: Nonlinear regression model linear in some parameters
negative_passages.docid: 10730
negative_passages.text: Proof that regression residual error is an unbiased estimate of error variance