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Research Journal of Economics & Business Studies - RJEBS - The ...

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By conducting multiple regression analysis, the study has shown how dependent variable changes<br />

according to the changes in independent variables. <strong>The</strong>refore, we can formulate the following<br />

Hypotheses:<br />

Ho: b1 = b2 = b3 =b4= ....…… = 0 (no relationship between dependent variable and independent<br />

variables)<br />

H1: b1 ≠ b2 ≠ b3 ≠ b4 ≠ 0 (significant relationship between dependent variable and independent<br />

variables)<br />

<strong>The</strong> basic model is given below:<br />

Human Development= a+ b1* Criteria based on brand + b2* Internship + b3* On campus interview +<br />

b4* Language club + b5* Knowledge based scholarship + b6* Campus atmosphere + b7* Own<br />

premises + b8* Workshop + b9* Computer Skill + b10* Training + b11* Satisfaction Regarding<br />

quality <strong>of</strong> education, Here, a= Constant; bi= Slope;<br />

Results and Analysis<br />

Factor Analysis<br />

To extract minimum number <strong>of</strong> factors, Principal Component Analysis (PCA) with varimax rotation<br />

was used. A Principal Component Analysis is a factor model in which the factors are based upon the<br />

total variance. In addition to selecting the factor model, we have specified how the factors are to be<br />

extracted in such a way that each factor is independent <strong>of</strong> all other factors. <strong>The</strong> Principle Component<br />

Analysis has used and total 11 variables have included in the factor analysis. At first correlation matrix<br />

has constructed and then tests the appropriateness <strong>of</strong> factor model. Bartlett’s test <strong>of</strong> Sphericity has been<br />

used to test the hypothesis that variables are uncorrelated in the population.<br />

Table 1: KMO and Bartlett's Test<br />

Kaiser-Meyer-Olkin Measure <strong>of</strong> Sampling Adequacy.<br />

Bartlett's Test <strong>of</strong> Sphericity Approx. Chi-Square 143.332<br />

df 55<br />

Sig. .000<br />

From the table, it is found that the approximate chi-square statistics is 143.332 with 55 degrees <strong>of</strong><br />

freedom which is significant at .05 levels and the value <strong>of</strong> KMO statistics (0.655) is also greater than<br />

.655<br />

www.theinternationaljournal.org > <strong>RJEBS</strong>: Volume: 02, Number: 08, June-2013 Page 35

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