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 = 3, y = 5, y = -1
Step-by-step explanation:
What you do is you substitute the x values into the x.
<em>y + 2(-1) = 5</em>
<em>y + -2 = 5</em>
y = 3
<em>y + 2(0) = 5</em>
y = 5
<em>y + 2(4) = 5</em>
<em>y + 6 = 5</em>
y = -1.
perimeter =QR+RS+ST+TU+UQ =26.1
RQ = 5.39
RS = 4.47
ST = 3.61
TU = 4.12
UQ = 8.54
