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:
-2r-19p
Step-by-step explanation:
-4p-2r-15p
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-2r-19p
Answer:
Null hypothesis: ∪ = No possible child abuse or neglect
Alternative hypothesis: Uₐ = Possible child abuse or neglect
Step-by-step explanation:
Null hypothesis: ∪ = No possible child abuse or neglect
Alternative hypothesis: Uₐ = Possible child abuse or neglect
A type I error occurs when you reject the null hypothesis when it is true. In this situation, a type I error occurs when you conclude on possible child neglect or abuse and place the child in protective custody
A type II error occurs when you accept the null hypothesis when it is false. In this instance, a type II error occurs when you conclude on no possible child abuse or neglect when there is and fail to remove the child from the home.
In this case, the type II error is the more serious error. Failure to remove the child when there is possible child abuse or neglect will lead to more detrimental effect. Although, the type I error is also serious, it is not so detrimental as the type II error.
