The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regression model analysis are
- There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
- There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
- The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
- The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
- The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
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Answer:
Repeats itself
Has a minimum or maximum
Multiplying by the same number
Step-by-step explanation:
Answer:
6
Step-by-step explanation:
All of them. (6)
The equasion has only one solution.
Answer:
8g
Step-by-step explanation:
In 8g, g has the coefficient 8: we multiply g by 8.
