Linear fit

A linear model describes the relationship between a continuous response variable and one or more explanatory variables using a linear function.

• Simple regression models
  Simple regression models describe the relationship between a single predictor variable and a response variable.
• Advanced models
  Advanced models describe the relationship between a response variable and multiple predictor terms.
• Scatter plot
  A scatter plot shows the relationship between variables.
• Summary of fit
  R² and similar statistics measure how much variability is explained by the model.
• Parameter estimates
  Parameter estimates (also called coefficients) are the change in the response associated with a one-unit change of the predictor, all other predictors being held constant.
• Effect of model hypothesis test
  An F-test formally tests the hypothesis of whether the model fits the data better than no model.
• Predicted against actual Y plot
  A predicted against actual plot shows the effect of the model and compares it against the null model.
• Lack of Fit
  An F-test or X²-test formally tests how well the model fits the data.
• Effect of terms hypothesis test
  An F-test formally tests whether a term contributes to the model.
• Effect leverage plot
  An effect leverage plot, also known as added variable plot or partial regression leverage plot, shows the unique effect of a term in the model.
• Effect means
  Effect means are least-squares estimates predicted by the model for each combination of levels in a categorical term, adjusted for the other model effects.
• Multiple comparisons
  Multiple comparisons make simultaneous inferences about a set of parameters.
• Residual plot
  A residual plot shows the difference between the observed response and the fitted response values.
• Residuals - normality
  Normality is the assumption that the underlying residuals are normally distributed, or approximately so.
• Residuals - independence
  Autocorrelation occurs when the residuals are not independent of each other. That is, when the value of e[i+1] is not independent from e[i].
• Outlier and influence plot
  An influence plot shows the outlyingness, leverage, and influence of each case.
• Prediction
  Prediction is the use of the model to predict the population mean or value of an individual future observation, at specific values of the predictors. Inverse prediction deals with the problem of predicting the value of a predictor for a given value of the response variable.