In this assignment, the relationship between the operating revenue of the construction companies in three countries (Denmark, Germany and Ukrain) and the three construction levels (A, B, and C) were investigate. The assignment contained two section. In section one, a one way Anova was conducted to investigate the difference in the mean among the three level of construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factors. In the second section, the two way analysis of variance ANCOVA was conducted to investigate the mean difference of the operating revenue of construction industry among the three countries and among the construction sectors. In performing the one way analysis of variance (ANOVA) one need one dependent variable which in this assignment was the operating revenue of the construction industry and one independent variable which is a category or factor variable which was the company independence factor.
The assignment had the following variables; dependent variable operating revenue of construction companies, and independent variables are, construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factor A, and the countries. The sample used in the assignment was 294. The countries used in the assignment for the data were Denmark, Germany and Ukraine). In the analysis of One way analysis of variance and two way analysis of variance, normality of data is assumed. The other assumption in one way analysis of variance and two way analysis of variance in the equality of variance and the last assumption is the linearity of independence with dependence variance. The data was obtained from the university database on ORBIS.
Results
2 (i) test of one way analysis of variance to determine if there is significant mead difference of the operating revenue among the level of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factors (A, B, C). In this section we shall conduct the test of homogeneity (equality of variance) using the levene test.
The levene test of homogeneity
Hypothesis 1
The null hypothesis: The variance of the residual are equal (homogeneous)
The alternative hypothesis: the variance of the residua are not equal
Test of Homogeneity of Variances 

OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 

Levene Statistic 
df1 
df2 
Sig. 
13.305 
2 
292 
.000 
From the analysis, the levene statistic was equal to 13.305 with a p value equal 0.000 which was less than 0.05 significant level. This means that the assumption of homogeneity of the variance among the factors was violated. In the assignment we reject the hypothesis that the homogeneity of variance was not violated.
The test for the main effect
Hypothesis 1
The null hypothesis: The mean operating revenue of the construction companies were no significantly different among the three factor of construction industry sector (A= construction of building, B= civil engineering, C=specialized construction).
The alternative hypothesis: The mean operating revenue of the construction companies were significantly different among the three factor of construction industry sector (A= construction of building, B= civil engineering, C=specialized construction).
From the analysis of the main effect, it can be observed that the f value was equal to 4.515 with a pvalue= 0.012 which was less than 0.05 significant level. This means that there was sufficient evidence to conclude that mean operating revenue of construction companies are significantly difference among the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction) factor A ( all the three levels of companies independence). At 0.05 significant level, the sample used in the assignment supported the alternative hypothesis that the operating revenue of construction population means are significantly different among the three level of the company independence.
ANOVA 

OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 


Sum of Squares 
Df 
Mean Square 
F 
Sig. 
Between Groups 
741152019065435.900 
2 
370576009532717.940 
4.515 
.012 
Within Groups 
23966264747945324.000 
292 
82076249136799.060 


Total 
24707416767010760.000 
294 



Post hoc analysis
Hypothesis 1
The null hypothesis: There is significant difference in the mean between different construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factor (A, B, C)
The alternative hypothesis: There is significant difference in the mean between different construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factor (A, B, C)
From the analysis of difference in means of the operating revenue between different construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s, it can be observed that the difference in mean between level A and Level B means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction) factors had a p value of 0.160 which was greater than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level A and Level B means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are not significantly different. The analysis indicated that the difference in mean between level A and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s had a p value of 0.001 which was less than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level A and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are significantly different. Finally, the analysis indicated that the difference in mean between level B and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s had a p value of 0.023 which was less than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level B and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are significantly different
Multiple Comparisons 

Dependent Variable: OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 


(I) FACTOR A 
(J) FACTOR A 
Mean Difference (IJ) 
Std. Error 
Sig. 
95% Confidence Interval 


Lower Bound 
Upper Bound 

LSD 
A 
B 
1689267.540 
1200471.126 
.160 
4051940.46 
673405.38 
C 
5616723.207* 
1661303.652 
.001 
8886370.49 
2347075.93 

B 
A 
1689267.540 
1200471.126 
.160 
673405.38 
4051940.46 

C 
3927455.667* 
1716945.959 
.023 
7306613.76 
548297.57 

C 
A 
5616723.207* 
1661303.652 
.001 
2347075.93 
8886370.49 

B 
3927455.667* 
1716945.959 
.023 
548297.57 
7306613.76 

GamesHowell 
A 
B 
1689267.540 
1040438.743 
.239 
4152634.33 
774099.25 
C 
5616723.207 
2652031.997 
.098 
12054596.23 
821149.82 

B 
A 
1689267.540 
1040438.743 
.239 
774099.25 
4152634.33 

C 
3927455.667 
2791129.139 
.345 
10659966.92 
2805055.58 

C 
A 
5616723.207 
2652031.997 
.098 
821149.82 
12054596.23 

B 
3927455.667 
2791129.139 
.345 
2805055.58 
10659966.92 

*. The mean difference is significant at the 0.05 level. 
2 (ii). The two way analysis of variance (ANOVA). In testing that the means operating revenue are significantly different among all categories of factor A (construction industry sector ) and factor B (different countries). The two way analysis of variance allow the test of the interaction effect.
The homogeneity test of variance
Hypothesis 1
The null hypothesis: The variance of the residual are equal (homogeneous)
The alternative hypothesis: the variance of the residual are not equal
Levene's Test of Equality of Error Variancesa 

Dependent Variable: OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 

F 
df1 
df2 
Sig. 
4.831 
8 
286 
.000 
Tests the null hypothesis that the error variance of the dependent variable is equal across groups. 

a. Design: Intercept + GROUP + FACTOR + GROUP * FACTOR 
From the analysis, the levene statistic was equal to 4.831 with a p value equal 0.000 which was less than 0.05 significant level. This means that the assumption of homogeneity of the variance among the factors was violated. In the assignment we reject the hypothesis that the homogeneity of variance was not violated.
The test for the main effect
Hypothesis 1
The null hypothesis: There is no interaction effect between the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factor and the country factor B
The alternative hypothesis: There is interaction effect between the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factor and the country factor B
From the analysis of the two way ANOVA, it was observed that the interaction effect between the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factor and the country factor B had an F value of 0.531 with a p value of 0.731 which was greater than 0.05 significant level. This means that the sample had sufficient evidence that the interaction effect between the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factor and the country factor B were not significant
Hypothesis 2
The null hypothesis: The mean operating revenue of the construction companies were no significantly different among the three factor of construction industry sector (A= construction of building, B= civil engineering, C=specialized construction).
The alternative hypothesis: The mean operating revenue of the construction companies were significantly different among the three factor of construction industry sector (A= construction of building, B= civil engineering, C=specialized construction).
From the analysis of the mean operating revenue among difference levels of construction industry sector , it was observed that the F value was 4.582 with a p value = 0.011 which was less than 0.05 significant level. This means that there was sufficient evidence that the mean operating revenue was significantly mean operating revenue difference among different levels of the construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factor. At 0.05 significant level the evidence indicate that the sample reject the null hypothesis that mean operating revenue of the construction companies were no significantly different among the three factor of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction).
Hypothesis 2
The null hypothesis: The mean operating revenue of the construction companies were no significantly different among the three countries
The alternative hypothesis: The mean operating revenue of the construction companies were significantly different among the three countries.
From the analysis of the mean operating revenue among difference countries, it was observed that the F value was 0.094 with a p value = 0.910 which was greater than 0.05 significant level. This means that there was sufficient evidence that the mean operating revenue was significantly mean operating revenue difference among different countries. At 0.05 significant level the evidence indicate that the sample reject the null hypothesis that mean operating revenue of the construction companies were no significantly different among the three counties.
Tests of BetweenSubjects Effects 

Dependent Variable: OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 

Source 
Type III Sum of Squares 
Df 
Mean Square 
F 
Sig. 
Corrected Model 
949536563579756.000a 
8 
118692070447469.500 
1.429 
.184 
Intercept 
2601916732160287.000 
1 
2601916732160287.000 
31.322 
.000 
GROUP 
15637334689748.379 
2 
7818667344874.189 
.094 
.910 
FACTOR 
761186306754776.200 
2 
380593153377388.100 
4.582 
.011 
GROUP * FACTOR 
176305375827990.300 
4 
44076343956997.580 
.531 
.713 
Error 
23757880203431020.000 
286 
83069511200807.770 


Total 
26577319294676120.000 
295 



Corrected Total 
24707416767010776.000 
294 



a. R Squared = .038 (Adjusted R Squared = .012) 
From the analysis of difference in means of the operating revenue between different construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s, it can be observed that the difference in mean between country 1 and country 2 means operating revenue had a p value of 0.776 which was greater than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level A and Level B means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are not significantly different. The analysis indicated that the difference in mean between country 1 and country 3 means of operating revenue had a p value of 0.900 which was greater than 0.05 significant level. This means that there is no sufficient evidence to conclude that the difference in means operating revenue between country 1 and country 2 means are significantly different. Finally, the analysis indicated that the difference in mean between country 2 and country 3 means operating revenue had a p value of 0.671 which was less than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between country 2 and country 3 means operating revenue are significantly different
Pairwise Comparisons 

Dependent Variable: OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 

(I) FACTOR B 
(J) FACTOR B 
Mean Difference (IJ) 
Std. Error 
Sig.a 
95% Confidence Interval for Differencea 

Lower Bound 
Upper Bound 

COUNTRY 1 
COUNTRY 2 
433700.113 
1522611.959 
.776 
3430646.963 
2563246.736 
COUNTRY 3 
188631.458 
1493941.494 
.900 
2751883.508 
3129146.425 

COUNTRY 2 
COUNTRY 1 
433700.113 
1522611.959 
.776 
2563246.736 
3430646.963 
COUNTRY 3 
622331.572 
1461586.681 
.671 
2254499.634 
3499162.777 

COUNTRY 3 
COUNTRY 1 
188631.458 
1493941.494 
.900 
3129146.425 
2751883.508 
COUNTRY 2 
622331.572 
1461586.681 
.671 
3499162.777 
2254499.634 

Based on estimated marginal means 

a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). 
Hypothesis 1
The null hypothesis: There is significant difference in the mean between different construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factor (A, B, C)
The alternative hypothesis: There is significant difference in the mean between different construction industry sector (A= construction of building, B= civil engineering, C=specialized construction) factor (A, B, C)
From the analysis of difference in means of the operating revenue between different construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s, it can be observed that the difference in mean between level A and Level B means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)factors had a p value of 0.543 which was greater than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level A and Level B means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are not significantly different. The analysis indicated that the difference in mean between level A and Level C& D means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s had a p value of 0.003 which was less than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level A and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are significantly different. Finally, the analysis indicated that the difference in mean between level B and Level C& D means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s had a p value of 0.013 which was less than 0.05 significant level. This means that there is sufficient evidence to conclude that the difference in means operating revenue between level B and Level C means of construction industry sector ( A= construction of building, B= civil engineering, C=specialized construction)s are significantly different
Pairwise Comparisons 

Dependent Variable: OPERATING REVENUEOF THE CONTRUCTION INDUSTRY 

(I) FACTOR A 
(J) FACTOR A 
Mean Difference (IJ) 
Std. Error 
Sig.b 
95% Confidence Interval for Differenceb 

Lower Bound 
Upper Bound 

A 
B 
704203.833 
1156679.046 
.543 
2980887.397 
1572479.731 
C 
4836478.948* 
1608464.474 
.003 
8002408.722 
1670549.173 

B 
A 
704203.833 
1156679.046 
.543 
1572479.731 
2980887.397 
C 
4132275.115* 
1661739.936 
.013 
7403066.623 
861483.607 

C 
A 
4836478.948* 
1608464.474 
.003 
1670549.173 
8002408.722 
B 
4132275.115* 
1661739.936 
.013 
861483.607 
7403066.623 

Based on estimated marginal means 

*. The mean difference is significant at the .05 level. 

b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). 

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