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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A, because anything with a dilation will not be congruent as the original PQR :))
9514 1404 393
Answer:
C. (9, 18)
Step-by-step explanation:
Given R(3, 5), a dilation by a factor of 3 will multiply each coordinate to give ...
(9, 15)
Then the translation up by 3 will give you the image coordinates ...
(x, y) ⇒ (x, y+3)
(9, 15) ⇒ (9, 15 +3) = (9, 18)
The coordinates of R' are (9, 18), matching choice C.
_____
<em>Comment on multiple choice answers</em>
As it often does, here it works to "guess" the answer that has the coordinates that are most-repeated. An x-coordinate of 9 is part of 3 answer choices; a y-coordinate of 18 is the only repeated value in the answer choices. If you were to guess, an appropriate guess would be (9, 18). That happens to be correct in this case.
Answer:
19y+5
Step-by-step explanation:
Combine like terms look for all the numbers that have y attached and combine them
7y +12y=19y
Look for terms where nothing is attached it's just the number combine them
6-1=5
Put all together again and you get:
19y+5
