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Res 342 Week 4 DQ 1

Res 342 Week 4 DQ 1

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Week 4 DQ 1: What are the requirements that must be met for a regression analysis?

What happens if these requirements are violated? Why is analysis of residuals

important?

The requirements that must be met for a regression analysis follows: The regression

analysis is dependent on the form of data being generated in relationship to the regression

approach used. When the data being generated is unknown, regression analysis may require

assumptions. One must be able in some cases to make assumptions about the process being

used. When these requirements are not met are violated there is a negative effect on data, and

the regression techniques. The effect of the violation leads to deceptive results or misleading

information.

Analysis of residuals is important because they can be used to test assumptions such

as are the errors normally distributed, the errors are independent, or they are non-auto

correlated, and the errors have constant variance or they are homoscedastic. Residuals ei are

great for recognizing clues about violations; regression residuals may violate one or more of the

assumptions. Whenever there are assumptions about possible violation, the OLS method will

make assumptions about the random error term É›i. Although É›i is not noticeable, residuals ei

would reveal clues of the violation.

(MathWorks 2010) stated “Residuals are differences between the one-step-predicted

output from the model and the measured output from the validation data set. Thus, residuals

represent the portion of the validation data not explained by the model. Residual analysis

consists of two tests: the whiteness test and the independence test. According to the whiteness

test criteria, a good model has the residual autocorrelation function inside the confidence

interval of the corresponding estimates, indicating that the residuals are uncorrelated. Your

model should pass both the whiteness and the independence tests, except in the following

cases: For output-error (OE) models and when using instrumental-variable (IV) methods, make

sure that your model shows independence of e and u, and pay less attention to the results of

the whiteness of e. In this case, the modeling focus is on the dynamics G and not the

disturbance properties H. Correlation between residuals and input for negative lags, is not

necessarily an indication of an inaccurate model. When current residuals at time t affect future

input values, there might be feedback in your system. In the case of feedback, concentrate on

the positive lags in the cross-correlation plot during model validation.”

References

Levine, D.M., Berenson, M.L., & Stephan, D. (1999). Statistics for mangers using microsoft

excel (2nd ed.). Upper Saddle River, New Jersey: Prentice Hall.

Mason, R., Lind, D., & Marchal, B. (1999). Statistical techniques in business and economics

(10th ed.). Boston: Irwin McGraw-Hill.

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Week 4 DQ 1: What are the requirements that must be met for a regression analysis? What happens if these requirements are violated? Why is analysis of residuals important? The requirements that must be met for a regression analysis follows: The regression analysis is dependent on the form of data being generated in relationship to the regression approach used.??When the data being generated is unknown, regression analysis may require assumptions.??One must be able in some cases to make assumptions about the process being used.??When these requirements are not met are violated there is a nega ...
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