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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When you take this problem and simplify it you get 9p-3
Answer:
HI ≈ 12.22
Step-by-step explanation:
tan I = GH / HI
tan 42° = 11 / HI = 0.90
HI = 11/0.90 = 12.22
Answer:
the answer is 1¢26 if not try h5
Perimeter of the whole shabang should be (7*2)+(5*2)