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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Just something that is true or are there choices? If just something true then dialated by 1 is always the same as the original shape.
Answer:
Area = 1500 
Step-by-step explanation:
Area= height ×(Base 1 +base 2)
Area = 20 × (40+35)
Area = 20×75
Area = 1500
Answer:
c
Step-by-step explanation:
