response variable वाक्य
उदाहरण वाक्य
मोबाइल
- Often the response variable may not be continuous but rather discrete.
- One complication is how to best deal with the response variable.
- However, these assumptions are inappropriate for some types of response variables.
- Measurements of patient deaths and harm are often used as response variables.
- Is the " i " th predicted value of the response variable.
- The form of the distribution assumed for the response variable y, is very general.
- Such implementations also allow use of truncated distributions and censored ( or interval ) response variables.
- However, it is not equivariant under affine transformations of both the predictor and response variables.
- This requirement then implies that one must first specify the distribution of the response variables observed.
- The logarithm of the expected value of the response variable is a linear combination of the explanatory variables.
- The primary analysis task is approached by fitting a regression model where the tip rate as the response variable.
- This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters.
- Multiple regression ( above ) is generally used when the response variable is continuous and has an unbounded range.
- Suppose we expect a response variable to be determined by a linear combination of a subset of potential covariates.
- GAMLSS is especially suited for modelling a leptokurtic or platykurtic and / or positively or negatively skewed response variable.
- In general, mathematicians and statisticians are good at visualizing relations among 2 predictor variables and one response variable.
- This assumption works well when the response variable and the predictor variable are jointly Normal, see Normal distribution.
- The response variable results from an " incomplete measurement " of, where one only determines the interval into which falls:
- The response variable may be non-continuous ( " limited " to lie on some subset of the real line ).
- In regression problems, the explanatory variables are often fixed, or at least observed with more control than the response variable.
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