| 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.
|
