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Independence of observations
An important assumption for the simple linear regression model is the independence of errors. In many time series models, this assumption is violated because of the correlation of errors in successive observations. This is termed to as autocorrelation.
Autocorrelation occurs if a positive error is followed by another positive error and a negative error is followed by another negative error. If autocorrelation occurs then time should be considered as an important independent variable and therefore time varies analysis should be used.
We can use Durbin Watson ‘D’ statistics to determine whether observations are independent.D = ∑(ei – ei-1)2 ∑ei2Where:
ei is the error in time iei-I is the error in time i-IThe Durbin Watson statistics provides a measure of association between successive values of the error term. The computed statistics is compared against two tabulated values du and dl that depend on the desired confidence level of the test and the degrees of freedom of the data.
If the computed Durbin Watson “D” statistics is greater than Du, then we can conclude that there’s no positive correlation between error terms.
If dl ≤ D≤ du then the test is inconclusive and therefore we can neither accept nor reject the null hypothesis.
First Cut Analysis of Costs The allocation of costs and assets will produce a value chain that illustrates graphically the distribution of a firm's costs. It can prove reveali
Ask question #MinimumYears Purchase Costs Running cost discount factor 8% Running cost Savings PVS 0 -7000 -7000 1 2000 0.926 1852 5556 3704 2 2500 0.857 2142.5 5999 3856.5
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