Answer:
OPTION A .
A.144 PLEASE MARK BRAINLEST
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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Step-by-step explanation (Question 1):
<u>Step 1: (Question 1): Subtract x from both sides.</u>
5x+3=x+13
5x+3−x=x+13−x
4x+3=13
<u>Step 2: (Question 1): Subtract 3 from both sides.</u>
4x+3−3=13−3
4x=10
<u>Step 3: (Question 1): Divide both sides by 4.</u>
4x/4 = 10/4
FIRST ANSWER: x = 5/2
Step-by-step explanation (Question 2):
<u>Step 1: (Question 2): Multiply by LCM</u>
15x^2 - 6 = x
<u>Step 2: (Question 2): Solve 15x^2</u>
15x^2 - 6 = x:
x = 2/3
x = -3/5
<u>Step 3: (Question 2): Solve</u>
ANSWER: x=0.666667 or x=−0.6
See Attachment 1 for question 1 steps (FULL)
See Attachment 2 for question 2 steps (FULL)
Answer:
1) x = 5/2
2) x=0.666667 or x=−0.6
Hope this helps.
Answer:
C. The slope is 3 and the y-intercept is -3