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.
Learn more about regression model here
brainly.com/question/15408346
#SPJ4
Answer:
Height of prism = 7 inches
Step-by-step explanation:
Let h be the height of prism.
Hence,
Answer:
d
Step-by-step explanation:
Using the Cosine rule to find ∠ A
cosA = 
= 
= 
= 
, then
∠ A = 
( ) ≈ 84.9° ( to 1 dec. place )
Answer:
No . C = COEFFICIENT IS THE ANSWER OF THE QUESTION
