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:
13
Step-by-step explanation:
3 x 3 = 9
7 + 3 = 10
7 + ? = 3 + 7 + 3 + 7
7 + ? = 20
? = 13
Answer is answer 17 plus 78
Answer:
4
Step-by-step explanation:
To find the square of a number, you multiply it by itself. This means √4 * √4 gives the answer. √4 can be simplified to 2 because 2^2 is 4. So, 2 *2 gives the answer, which is 4.
