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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This are the right steps
Step 1: first you divide the both size by 7 because there is 7 a's
7a/7 = 28/7
Step 2: You solve the equation
7a/7= a. 28/7 = 4
So, the answer is a = 4
not 7 = 4
The answer is 80.5 ... 805 / 5
Answer:
6/3 or 2 (you should probably write 2 though)
(there’s a couple notes in the pic too that will help you with slopes)
Like wat is it like i need was you need to show me your Question so i know what you talking about
