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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Three hundred ninety six divided by twenty four = 16.5
Answer:
x = 2
Step-by-step explanation:
Given
- 7x - 3x + 2 = - 8x - 2 ← simplify left side
- 10x + 2 = - 8x - 2 ( add 8x to both sides )
- 2x + 2 = - 2 ( subtract 2 from both sides )
- 2x = - 4 ( divide both sides by - 2 )
x = 2
Answer:
The life expectancy of a circulating coin is 25 years. The life expectancy of a circulating dollar bill is only 1/20 as long.
Step-by-step explanation:
Answer:
A
Step-by-step explanation:
It would be A, because EQUAL lateral is all sides are equal
