Regression Analysis Interpretation - Paper Example

Introduction

From the Regression analysis, only five of the sampled countries revealed a significant relationship between the development of the country and the scores. These countries are Algeria, Israel, Sudan, Syria and Tunisia.

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To start with, the Coefficient for development in Algeria is positive at 3.168e+00 which is significant at <2e-16. This shows that a unit increase in development causes a consecutive increase in the score level by 3.168e+00 (Bolin, 2014). Therefore the country can focus on development to proportionately improve their score.

The coefficient for development in Israel is positive at 1.332e-01 which is significant at 0.00368. This means that a unit increase in development in the country causes a consecutive increase in the score level by 1.332e-01(Hayes, and Andrew, 2013). This explains that if Israel was to increase its score, it has to increase the development level by one unit.

The coefficient for development in Sudan is negative at 1.034e-01 which is significant as at 0.01464. This means that a unit increase in development in the country causes a consecutive decrease in the score levels by 1.034e-01. Hence despite the country's effort in development, there is a decrease in the score level. This can be explained by other independent factors.

The coefficient for development in Syria is positive at 1.296e-01 which is significant at 0.00352. This explains that a consecutive increase in development in Syria by one unit will cause a proportionate increase in the scores level by 1.296e-01. This, therefore, means that the country can focus on development to increase its score level.

The last country is Tunisia whose coefficient for development is positive at 1.136e-01 which is significant at 0.00882 (Mason, & Perreault, 1991). This means that a unit increase in development level in Syria will cause a proportionate increase in the scores level by 1,136e-01.

The Gini coefficient 1.691e-01 significant at <2e-16 shows the level of score in the model when other factors are held constant, i.e. 1.691e-01.

The coefficient for multiple linear regression R2=0.3341 shows that approximately 33.41% of the researched factors explained the dependent variable (score level) showing that the model used had a medium variability when a linear regression line is plotted. The model used was of a good fit.

The F value 24.67 on 590df was significant at 2.2e-16 shows that the variable used thoroughly explained the dependent variable (score). Since this value is less than the significance level of 0.005, we reject the earlier stated null hypothesis and fail to reject the alternate hypothesis.

References

Bolin, J. H. (2014). Hayes, Andrew F.(2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A RegressionBased Approach. New York, NY: The Guilford Press. Journal of Educational Measurement, 51(3), 335-337.

Mason, C. H., & Perreault Jr, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of marketing research, 268-280.