Method of Detection Effects of the Departures/Violations on Assumptions violations on the Remedial Measures Graphical Statistical model 1. The values of the regressors, the X's, are not fixed or random, or X values are not independent of the error term. 2. There is exact linear relationship or exact collinearity between the X variables. 3. For given X's, the mean value of disturbance ɛ, is not equal to zero. 4. For given X's, there is autocorrelation, or serial correlation, between the disturbances. 5. The number of observations n is less than or equal to the number of parameters to be estimated. 6. The model is incorrectly specified, so there is specification bias. 7. There are errors in the measurement of variables of the model.

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Method of Detection
Effects of the
Departures/Violations on Assumptions
violations on the
model
Remedial Measures
Graphical
Statistical
The values of the regressors, the
X's, are not fixed or random, or X
values are not independent of the
error term.
1.
2. There is exact linear relationship or
exact collinearity between the X
variables.
3. For given X's, the mean value of
disturbance ɛ, is not equal to zero.
4. For given X's, there is
autocorrelation, or serial correlation,
between the disturbances.
5. The number of observations n is less
than or equal to the number of
parameters to be estimated.
6. The model is incorrectly specified, so
there is specification bias.
7. There are errors in the measurement
of variables of the model.
Transcribed Image Text:Method of Detection Effects of the Departures/Violations on Assumptions violations on the model Remedial Measures Graphical Statistical The values of the regressors, the X's, are not fixed or random, or X values are not independent of the error term. 1. 2. There is exact linear relationship or exact collinearity between the X variables. 3. For given X's, the mean value of disturbance ɛ, is not equal to zero. 4. For given X's, there is autocorrelation, or serial correlation, between the disturbances. 5. The number of observations n is less than or equal to the number of parameters to be estimated. 6. The model is incorrectly specified, so there is specification bias. 7. There are errors in the measurement of variables of the model.
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