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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Input each points. (x,y) for each equation to find out if the system of equations are equivalent.
Point (-3,4) is a solution of this system of equations.
Point (-2,-6) isn't a solution of this system of equations.
Point (-4,3) is a solution of this system of equations.
<span>7N - 2N + 3P + 2P
Combine like terms:
7N-2N=5N
3P+2P=5P
Put them together to get :
5N+5P
Final answer:
A</span>
The answer is 10 because its in the tens place
Answer:
452.4 cm^2
Step-by-step explanation:
Use the area formula A = πr². With d = 24 cm, r = 12 cm, and so the desired area is
A = π(144 cm^2) = 452.4 cm^2
