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:
c
Step-by-step explanation:
bc im smart ffff f f g g g g g g. t t t t t
Answer:
greater than cb and less than ab ig
Answer:
a reflection and a dilation
Step-by-step explanation:
in the wording, it was stated that the triangle was reflected then dilated.
