BSM004 Computer lab
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Download data file (Regression_lab_data.xls) in computer lab session from moodle
Open Regression_lab_data.xls
Go to data -> data analysis
Select regression
 Select B2-B61 for Y
 Select C2 – C61, D2 – D61, E2 – E61 for Xs
 Tick residuals in Residual options
Click ok
Basic discussion on the regression result
Autocorrelation
 Graphic approach: (plot residual and first order residual)
 In regression result output spreadsheet, create (residual -1) in D28-D86 which is the
first-order residual value
 Select C28 – D86
 Go to insert -> scatter
 No clear relationship here
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In SPSS
Create Residual lag 1 variable with Compute variable option in Transform
Go to Graphs -> Legacy dialogs -> Scatter/Dot -> Simple Scatter
Set Y as Residual, X as Residual lag 1
Click ok
No clear relationship here
Formal test: (regression)
 In excel
o Select regression (constant is zero)
o Select C28-C86 for Y
o Select D28-D86 for Xs
o Select constant is zero and click ok
o From the regression result we can see the model failed the F test, R2 is very
small and the coefficient (p) is not significant
o Therefore no clear autocorrelation here
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In SPSS (DW test)
o Download data file (Regression_lab.sav) from Moodle
o Open SPSS, open the data file
o Go to analyse
o Under regression, select linear
o Input Y and Xs
o Click on statistics option on right top part of regression table
o Tick Durbin-Watson option in Residual
o Click continue, then click ok
o Now we have a separate window in result for DW test. The DW value is
1.891, which is quite close to 2. p = 0.0545 => positive relationship,
o dl and du at (1%, 60, 3) = 1.32, 1.52
o Compare DW with dl and du
o DW = 1.891 > du , we accept the H0, autocorrelation is not significant.
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In SPSS (Regression on first order autocorrelation)
o Create Residual lag 1
o Run regression (Y: Residual, X: Residual lag 1)
o In Option, unlick “include constant in equation”
o From the regression result we can see the model failed the F test, R2 is very
small and the coefficient (p) is not significant
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Therefore no clear autocorrelation here
Heterscedasticity
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Graphic approach (Plot residual)
 Create Square value of residual in E27-E86
 Select B27-B86, E27-E86
 Go to insert -> scatter -> scatter with only marks
 The plot reflects variance of the residual is not constant
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In SPSS
Create Residual squared variable with Compute variable option in Transform
Go to Graphs -> Legacy dialogs -> Scatter/Dot -> Simple Scatter
Set Y as Residual squared variable, X as predicted Y
Click ok
The variance is not constant
Formal test
 SPSS steps are the similar to details in excel file
 Save the residual and predicted y
 Use compute variables to create residual squared value, predicted y squared value and all
possible combination of independent variables
 Run regressions for LM and White test
 Check F test, or use n*Ru2
 Remember to use the correct m for χ2 test