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:
y= -4x+74
Step-by-step explanation:
hope that helped
Answer:
The answer is 17.
Step-by-step explanation:
I might be wrong but I think it's 50 minutes.
$0.10 x 50min = $5
$20 + $5 = $25
$0.20 x 50min = $10
$15 + $10 = $25
So, it's 50 minutes.
I would go with A but I am not 100% sure so you may want to check however you can.