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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(gof)(0) cannot be evaluated
<em><u>Solution:</u></em>
Given that,
A composite function is denoted by (g o f) (x) = g (f(x)).
The notation g o f is read as “g of f”
Therefore, let us find whether (gof)(0) can be evaluated or not
To find (gof)(0):
(g o f) (x) = g (f(x))
Now substitute the given value of f(x)
Now to find (gof)(0), substitute x = 0
Since 1 divided by 0 is undefined, because any number divided by 0 is undefined
(gof)(0) cannot be evaluated
Answer:
it says round to the nearest 10th so it wouldn't be 81, it would be 81.8%
The mean absolute deviation shows the average distance number data is from each other. The bigger it is the farther the numbers are away from each other and vice versa.
