//  File New-Lec2-03-01-01-04.txt. Edition 7/14/2010.


//  Lab Specs
//    Window Title
         Estimating_Variances
//    Option (0 Normal, 1 Auto, 2 Heter, 3 X-e correlation, 4 Measurement, 5 Consistency (Is this used?)
         0
//    Constant List
         40 60 10 50
//    Coefficient List
         -2 6 2 2
//    iListVarErrors
         50 500 150 500
//    iListSamples
         3 250 1 5
//    iListSamplesAutocorrelation
         3 50 1 40
//    iListSamplesMeasurement
         3 50 1 5
//    iListXMinima
         0 10 10 0
//    iListXMaxima
         20 30 10 30
//    strListFromCoef
         -1.0 4.0 .5 1.0
//    strListToCoef
         1.0 6.0 .5 3.0
//    strListRho
         -.9 .9 .3 .0
//    strListHeter
         -2.0 2.0 1.0 .0
//    strListCoefXAndError
         -.9 .9 .3 .0
//    iListXMeasErrVar
         0 100 50 0

//  Problem Specs
//    SampleSize
//    PauseCheckbox (-1 checked, 0 cleared)
//    EstType (0 Error, 1 Constant, 2 Coefficient)
//    ErrorVar
//    ConstValue
//    CoefValue 
//    xMinValue
//    xMaxValue
//    ErrTermCheckbox (0 not visible and unchecked, -1 visible and checked, 1 visible and unchecked))
//    VarEstAndFromTo (-1 VarEst visible, 0 nothing visible, 1 From-To visible)



` 3 -1 0 500 50 2 0 30 0 -1
Objective: Show that in any repetition, the sum of squared residuals,
 SSR, will never be greater than the sum of squared errors, SSE.
_
1. Click Start. What do the sum of squared errors and the sum of 
 squared residuals equal in for the first repetition? Which is smaller? 
_
2. Click Continue to run the second repetition. What do the sum of squared
 errors and the sum of squared residuals equal in the second repetition? Which
 is smaller?

`
3. Click Continue a few more times until you convince yourself that the
 sum of squared errors is never less than the sum of squared errors.
_
4. What criterion does the ordinary least squares (OLS) estimation procedure use to 
 compute the estimates constant and coefficient? Does the criterion explain why
 the sum of squared is never less than the sum of squared residuals.