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:3
Explanation: 3x5=15+12=27-5=22
Answer:
This is false.
Step-by-step explanation:
90 + 12 = 102.
6(15+12) equals to 6(27) which is 162.
Answer:
160,160
Step-by-step explanation:
let the first page be X
second one be x+1
x+x+1=321
2x=320
x= 320÷2=160
pages 160 161
8• (5+x) < 56
That is the equation