Answer:
What are the equations and anwsers?
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:
Painting USA is cheaper. They cost $700 dollars. The other place costs $1,000 dollars
Answer:
x=0
Step-by-step explanation:
-8-3x=-8(1-7x)
add 8 to both sides =( -3x=56x)
subtract 56x from both sides (-59x=0)
divide both sides by -59 = (x=0)
