Remedy Serial Correlation Problems

As previously stated, most serial correlatirai problems point to the need for another, or several values for the x, variable. For instance, in Example 3.1, the sterilization hip joint study, looking at the data, the researcher noted that the temperature fluctuation in the steam vessel was 2.0°C. In this type of study, a range of 4.0°C can be very influential. For example, as x,, x,y and x,j are measured, the bier vessel cycles throughout the +2.0°C range. The serial correlation would tend to appear positive due to the very closely related temperature fluctuations. A way to correct this situation partially would be to add another regression variable, X2, representing temperature. The model would then be 

Note that efforts described above would not take care of the major problem, that is x, to some degree, is determined by x, i, which is somewhat determined by x, 2 and so on. Forecasting methods, such as moving averages, are better in these situations. 

Transformation Procedure (When Adding More Predictor Xj Values Is Not an Option) 

When deciding to measure correlated error (e,) values (lag 1), remember that the y, values are the cause of this. Hence, any transformation must go to the root problem, the y,s. In the following, we also focus on lag 1 correlation. Other lags can be easily modeled from a 1 lag equation. Equation 3.7 presents the decomposition of y, the dependent y, variable, influenced by y, i 

With this transformation, the linear regression model, using the ordinary least-squares method of determination, is valid. However, to employ it, we need to know the population serial correlation coefficient, P. We estimate it by r. The population Equation 3.9 through Equation 3.11 will be changed to population estimates  

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