> rcorr(as.matrix(dsCase[c("Age", "HrPWork", "CO2CompMax")]))
             Age HrPWork CO2CompMax
Age         1.00    0.18      -0.03
HrPWork     0.18    1.00      -0.04
CO2CompMax -0.03   -0.04       1.00

n= 693


P
           Age    HrPWork CO2CompMax
Age               0.0000  0.5075
HrPWork    0.0000         0.3517
CO2CompMax 0.5075 0.3517
> pcor(dsCase[c("Age", "HrPWork", "CO2CompMax")])
$estimate
                   Age     HrPWork  CO2CompMax
Age         1.00000000  0.17808150 -0.01920447
HrPWork     0.17808150  1.00000000 -0.03143962
CO2CompMax -0.01920447 -0.03143962  1.00000000

$p.value
                    Age      HrPWork CO2CompMax
Age        0.000000e+00 2.430766e-06  0.6140339
HrPWork    2.430766e-06 0.000000e+00  0.4089424
CO2CompMax 6.140339e-01 4.089424e-01  0.0000000

$statistic
                  Age    HrPWork CO2CompMax
Age         0.0000000  4.7538043 -0.5045531
HrPWork     4.7538043  0.0000000 -0.8262597
CO2CompMax -0.5045531 -0.8262597  0.0000000

$n
[1] 693

$gp
[1] 1

$method
[1] "pearson"

> ResultatenVoor <- aov(CO2CompMax~dGender, data = dsCase)
> ResultatenNa <- aov(CO2CompMax~dGender + avgGuilt, data = dsCase)
> Anova(ResultatenVoor, type = c("III"))
Anova Table (Type III tests)

Response: CO2CompMax
             Sum Sq  Df F value  Pr(>F)
(Intercept)    1143   1  0.1847 0.66752
dGender       18074   1  2.9215 0.08786 .
Residuals   4274938 691
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Anova(ResultatenNa, type = c("III"))
Anova Table (Type III tests)

Response: CO2CompMax
             Sum Sq  Df F value Pr(>F)
(Intercept)    5715   1  0.9235 0.3369
dGender       15201   1  2.4561 0.1175
avgGuilt       4583   1  0.7405 0.3898
Residuals   4270355 690
> #eerst t-toets tussen dGender en CO2CompMax
> t.test(CO2CompMax ~ dGender, data = dsCase)

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -1.4879, df = 330.99, p-value = 0.1377
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -24.075010   3.339171
sample estimates:
mean in group 1 mean in group 2
       14.27145        24.63937

> #dan t-toets opsplitsen in categorieen van cLocate
> by(dsCase, as.factor(dsCase$fcLocate), function(x) t.test(CO2CompMax ~ dGender, data = x))
as.factor(dsCase$fcLocate): City

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -0.43985, df = 168.95, p-value = 0.6606
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -28.38187  18.03878
sample estimates:
mean in group 1 mean in group 2
       16.95809        22.12963

---------------------------------------------------------------------------------------
as.factor(dsCase$fcLocate): Suburban

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -0.75398, df = 49.868, p-value = 0.4544
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -45.66782  20.74082
sample estimates:
mean in group 1 mean in group 2
       15.12346        27.58696

---------------------------------------------------------------------------------------
as.factor(dsCase$fcLocate): Town

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -0.62649, df = 93.175, p-value = 0.5325
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -11.795974   6.137965
sample estimates:
mean in group 1 mean in group 2
       12.01948        14.84848

---------------------------------------------------------------------------------------
as.factor(dsCase$fcLocate): Village

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -1.2501, df = 67.456, p-value = 0.2156
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -31.273974   7.184621
sample estimates:
mean in group 1 mean in group 2
       10.68667        22.73134

---------------------------------------------------------------------------------------
as.factor(dsCase$fcLocate): Rural

	Welch Two Sample t-test

data:  CO2CompMax by dGender
t = -1.014, df = 5.0026, p-value = 0.3571
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -441.9456  191.8850
sample estimates:
mean in group 1 mean in group 2
       8.636364      133.666667

> anova(aov(CO2CompMax ~ cLocate * cLiving, data = dsCase))
Analysis of Variance Table

Response: CO2CompMax
                 Df  Sum Sq Mean Sq F value Pr(>F)
cLocate           1      68    68.0  0.0109 0.9168
cLiving           1      14    14.2  0.0023 0.9619
cLocate:cLiving   1    2253  2252.7  0.3617 0.5477
Residuals       689 4290677  6227.4
> anova(aov(CO2CompMax ~ cLocate + cLiving, data = dsCase))
Analysis of Variance Table

Response: CO2CompMax
           Df  Sum Sq Mean Sq F value Pr(>F)
cLocate     1      68    68.0  0.0109 0.9167
cLiving     1      14    14.2  0.0023 0.9619
Residuals 690 4292930  6221.6