Proceedings of the 3rd European Conference on Intellectual Capital

Simona Agoston et al.
Regression and correlation are closely related. Both techniques involve <strong>the</strong> relationship between two
variables, and <strong>the</strong>y both utilize <strong>the</strong> same set <strong>of</strong> paired scores taken from <strong>the</strong> same subjects. However,
whereas correlation is concerned with <strong>the</strong> magnitude and direction <strong>of</strong> <strong>the</strong> relationship, regression
focuses on using <strong>the</strong> relationship for prediction (Ho, 2006). The results <strong>of</strong> <strong>the</strong> linear regression are
presented in table 4, 5, 6.
Table 4: Model summary
Model
R
R Square
Adjusted R Square
Std. Error <strong>of</strong> <strong>the</strong> Estimate
1 .918 a .843 .790 453.58973
Table 5: ANOVA
1
Model
Sum <strong>of</strong> Squares
Df
Mean Square
Regression 3303101.858 1 3303101.858 16.054 .028 a
Residual 617230.942 3 205743.647
Total 3920332.800 4
a. Predictors: (Constant), Knowledge transfer Index
Table 6: Coefficients table
Unstandardized Coefficients
Standardized
Coefficients
Model B Std. Error Beta t Sig.
1
(Constant) 12175.947 1522.199 7.999 .004
Knowledge transfer Index -2135.953 533.082 -.918 -4.007 .028
The prediction equation is:
Y' = A + B * X (3)
where: Y' = <strong>the</strong> predicted dependent variable, A = Constant, B = unstandardized regression
coefficient, and X =value <strong>of</strong> <strong>the</strong> predictor variable. Using <strong>the</strong> constant and unstandardized coefficient
values, <strong>the</strong> prediction equation (3) would become:
Predicted Number <strong>of</strong> candidates = 12175.947 + (-2135.95) * Knowledge transfer index (4)
A measure <strong>of</strong> <strong>the</strong> strength <strong>of</strong> <strong>the</strong> computed equation is R-square, or coefficient <strong>of</strong> determination. Rsquare
represents <strong>the</strong> proportion <strong>of</strong> variance accounted for in <strong>the</strong> dependent variable (Number <strong>of</strong>
candidates) by <strong>the</strong> predictor variable (Index <strong>of</strong> knowledge transfer).
The ANOVA table presents results from <strong>the</strong> test <strong>of</strong> <strong>the</strong> null hypo<strong>the</strong>sis that R-square is zero which
would indicate no linear relationship between <strong>the</strong> predictor and dependent variable. The table shows
that <strong>the</strong> computed F statistic is 16.05, with an observed significance level <strong>of</strong> less than 0.05. Thus, <strong>the</strong>
hypo<strong>the</strong>sis that <strong>the</strong>re is no linear relationship between <strong>the</strong> predictor and dependent variable is
rejected.
Taken into consideration <strong>the</strong> test concluded (Pearson correlation coefficient, Spearman correlation
coefficient and <strong>the</strong> linear regression) we can deduce that <strong>the</strong> research hypo<strong>the</strong>sis (H1) is rejected and
accept <strong>the</strong> null hypo<strong>the</strong>sis (H0). Thus, <strong>the</strong> statistical test conducted proved <strong>the</strong> existence <strong>of</strong> a negative
correlation between <strong>the</strong> index <strong>of</strong> knowledge transfer <strong>of</strong> universities and <strong>the</strong> number <strong>of</strong> candidates for
<strong>the</strong> programs proposed.
Moreover, due to <strong>the</strong> fact that this study was a pilot study, conducted only on one university from <strong>the</strong>
area <strong>of</strong> Bucharest <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> results has to be fur<strong>the</strong>r analyzed and more factors have to be
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F
Sig.