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Model 2:
a)
Regression eqn:
Math = 14.193 + .339*IQ + 35.862*SchoolA – 3.12*SchoolB - .352*IQ_SchoolA + 0.051*IQ_SchoolB
b)
R Squared = .46.7%
Only 46.7% variation in dependent variable is explained by all independent variables.
c)
F ratio =.281
With p-value > 0.05, I can say that it is not significant.
d)
common b = 45.084. F-ratio = 141.339368
e)
R Squared = .080
f)
F - ratio = .639
With p-value > 0.05, I can say that F ratio for difference among schools after covarying IQ is not significant.
With p-value > 0.10, I can say that F ratio for difference among schools after covarying IQ is not significant.
Over all I can say that F ratio for difference among schools after covarying IQ is not significant.
g)
Math = 45.559 + .035*IQ + .482*SchoolA
Math = 46.994 + .034*IQ – 1.542*SchoolB
Math = 44.53 + .041*IQ + 1.086*SchoolC

h)
School A 1.479; School B 15.733; School C 7.749
SYNTAX AND OUTPUT
A)B)C)
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT math
/METHOD=ENTER IQ School_A sCHOOL_b IQ_SchoolA IQ_SchoolB.
Variables Entered/Removed
a

Model Variables Entered Variables
Removed
Method
1
IQ_SchoolB, IQ,
IQ_SchoolA,
School_A,
sCHOOL_b
b

. Enter
a. Dependent Variable: math
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .684
a
.467 .357 2.84314
a. Predictors: (Constant), IQ_SchoolB, IQ, IQ_SchoolA, School_A, sCHOOL_b
ANOVA
a

Model Sum of Squares df Mean Square F Sig.
1
Regression 170.297 5 34.059 4.213 .007
b

Residual 194.003 24 8.083
Total 364.300 29
a. Dependent Variable: math
b. Predictors: (Constant), IQ_SchoolB, IQ, IQ_SchoolA, School_A, sCHOOL_b
Coefficients
a

Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 14.193...