Category: Sum of Squares</strong
-
the Bivariate regression analysis revealed that the independent and dependent variables had a statistically significant relationship because Beta was.578
—
by
in .000(a)</td, .000</td, .000</td, .058</td, .234</td, .256</td, .376</td, .396</td, .53505</td, .578</td, .619(a)</td, .656</td, (Constant)</td, </p, </p, </p, </td, </td, </td, </td, </td, </td, </td, </td, </td, </td, </td, </td, </tr, </tr, </tr, </tr, <div class=""list-content"", <div class=""webkit-scrollbars webkit-scrollbars–table"", <div class=""webkit-scrollbars webkit-scrollbars–table"", <div class=""webkit-scrollbars webkit-scrollbars–table"", <em, <p, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td, <td colspan=""2"", <td colspan=""2"", <td colspan=""2"", <tr, <tr, <ul, 000.</li, 1.378</td, 1</td, 1</td, 16.672</td, 16.672</td, 2 </sup, 25.511</td, 42.183</td, 56.565</td, 6.897</td, 7.656</td, 94</td, 95</td, A Summary of Model.</em, Adjusted R Square</strong, ANOVA (b).</em, b</sup, B</td, B</td, Beta</td, Co-efficient (a).</em, Df</strong, df</td, F</strong, F</td, In general terms, Mean Square</strong, Mean Square</td, Model, Model</strong, R Square</strong, R</strong, Regression</td, Residual</td, Sig.</strong, Sig.</td, Sig.</td, Standardised Coefficients</strong, Std. Error of the Estimate</strong, Std. Error</td, Strategic Evaluation</h4, Sum of Squares</strong, Sum of Squares</td, t</td, Total</td, Unstandardised Coefficients</strong, was 0.396.</p, while R<sup, X3SE</tdwhile the outsourcing performance was the dependent variable. According to the table below