11. The primary analysis task is approached by fitting a regression model where the tip rate as the response variable . 12. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. 13. Multiple regression ( above ) is generally used when the response variable is continuous and has an unbounded range. 14. Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates. 15. GAMLSS is especially suited for modelling a leptokurtic or platykurtic and / or positively or negatively skewed response variable . 16. In general, mathematicians and statisticians are good at visualizing relations among 2 predictor variables and one response variable . 17. This assumption works well when the response variable and the predictor variable are jointly Normal, see Normal distribution. 18. The response variable results from an " incomplete measurement " of, where one only determines the interval into which falls: 19. The response variable may be non-continuous ( " limited " to lie on some subset of the real line ). 20. In regression problems, the explanatory variables are often fixed, or at least observed with more control than the response variable . 