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:
B)10
Step-by-step explanation:
Because this is a right angle(indicated by the red box) the total measure should be 90°.
And based on that we say 5X +40=90
Subtract 40 from both sides to get 5x=50
And then divide both sides by 5 to get x=10
This is not correct. It is not taking into account the 150 employees.
In a parallogram the two angles on the same side ( Angle T and Angle C) equal 180 degrees
So we have 8x +29 + 2x +11 = 180
combine the like terms:
10x + 40 = 180
Subtract 40 from each side:
10x = 140
Divide each side by 10:
X = 140 /10
X = 14
Now we have X, replace X into the equation for angle C
2(14) +11 = 28 + 11 = 39 degrees
