I got some results like

Multivariate Tests (Design: Intercept + haveinsure)

Effect Value F Hypothesis df Error df Sig.

Intercept Pillai's Trace .053 11.361(b) 3.000 470.000 .000

Wilks' Lambda .827 11.361(b) 3.000 470.000 .000

Hotelling's

Trace .069 11.361(b) 3.000 470.000 .000

Roy's Largest

Root .083 11.361(b) 3.000 470.000 .000

haveinsure Pillai's Trace .138 4.570 12.000 1420.000 .000

Wilks' Lambda .877 4.797 12.000 1248.086 .000

Hotelling's

Trace .151 4.998 12.000 1410.000 .000

Roy's Largest

Root .141 16.101(c) 4.000 473.000 .000

b - Exact statistic

c The statistic is an upper bound on F that yields a lower bound on the significance level

Tests of Between-Subjects Effects Tests

Source Dependent

Variable Type III df Mean F Sig.

Sum of Squares Square

Corrected Model age 37.546(a) 4 9.637 3.893 .004

education 10.619(b) 4 2.655 .477 .752

income 334.245(c) 4 84.061 16.766 .000

Intercept age 32.173 1 34.173 13.805 .000

education 141.268 1 143.268 25.752 .000

income 30.201 1 30.201 6.024 .014

haveinsure age 37.546 4 9.637 3.893 .004

education 10.619 4 2.655 .477 .752

income 335.245 4 84.061 16.766 .000

Error age 1171.320 474 2.475

education 2636.013 474 5.563

income 2375.494 474 5.014

Total age 3150.000 479

education 12315.000 479

income 6289.000 479

Corrected Total age 1210.866 478

education 2646.633 478

income 2711.739 478

a. R Squared = .032 (Adjusted R Squared = .024)

b. R Squared = .004 (Adjusted R Squared = -.004)

c. R Squared = .124 (Adjusted R Squared = .117)

Dependent Parameter B Std. t Sig. 95% Confidence Interval

Variable Error Lower Upper

Bound Bound

age Intercept 1 1.573 0.637 0.525 -2.092 4.092

[haveinsure=1] 1.173 1.576 0.745 0.456 -1.923 4.268

[haveinsure=2] 0.589 1.578 0.373 0.708 -2.514 3.693

education Intercept 4 2.358 1.697 0.091 -0.635 8.636

[haveinsure=1] 0.578 2.362 0.245 0.808 -4.063 5.219

[haveinsure=2] 0.388 2.367 0.164 0.87 -4.265 5.04

income Intercept 1 2.238 0.448 0.659 -3.4 5.4

[haveinsure=1] 2.289 2.242 1.021 0.309 -2.118 6.696

[haveinsure=2] 0.419 2.245 0.188 0.852 -3.999 4.837

1. Am I approaching the problem in a proper way? I mean am I doing the right analysis in SPSS?

2. Which method(Pillai's Trace, Wilks' Lambda, Hotelling's Trace, Roy's Largest Root) should be used for a case like mine?

3. Why is Type III Sum of Squares error 1171.320 for age, education and income?

4. I am new to Multivariate linear regression analysis. How can I interpret and learn more about the output SPSS generated?

Any suggestions would be appreciated.

Thanks