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: 2 1/4
Step-by-step explanation:
U=2x+3, x=u/2-3/2
u^2+8u+11=0
u1=(-8+sqrt(64-44))/2=-4+sqrt(5)
u2=-4-sqrt(5)
x1=u1/2-3/2=-7/2+sqrt(5)/2
x2=u2-3/2=-7/2-sqrt(5)/2
Answer:
x=0.5355 or x=-6.5355
First step is to: Isolate the constant term by adding 7 to both sides
Step-by-step explanation:
We want to solve this equation: 
On observation, the trinomial is not factorizable so we use the Completing the square method.
Step 1: Isolate the constant term by adding 7 to both sides
Step 2: Divide the equation all through by the coefficient of 
which is 2.
Step 3: Divide the coefficient of x by 2, square it and add it to both sides.
Coefficient of x=6
Divided by 2=3
Square of 3=
Therefore, we have:
Step 4: Write the Left Hand side in the form 
Step 5: Take the square root of both sides and solve for x
