Regression Analysis - Essay Example

Lastly, regression analysis will be performed on statistically significant pairs of variables and regression line equation(s) of best fit will be written. Using Excel’s Data Analysis-Sampling, random number for data for country was generated (Figure 1) and corresponding records were taken from population. Table 1 in appendix shows the sample for correlation and regression analysis. Figure 3 shows the scatterplot of the GNP per head and the number of commercial vehicles. The relationship between the GNP per head and the number of commercial vehicles appears to be moderate (correlation, r = 0.556). Table 2 shows the correlation matrix for variables GNP per head, Cars per head and number of Commercial vehicles.

The correlation for ‘the GNP per head and the cars per head’ and ‘the GNP per head and the number of commercial vehicles’ is significant at level of significance, α = .01. Since, the correlation for GNP per head with the cars per head and the number of commercial vehicles is significant; therefore, there is strong possibility that GNP per head can be used to predict the cars per head and the number of commercial vehicles in any country. Table 3 shows the regression analysis output for the GNP per head and the cars per head using Excel.

The higher value of F statistic suggests that regression is significant. GNP per head explains about 80% of variation in the cars per head. GNP per head (US $) significantly predict the cars per head, β = 0.00005, t(28) = 10.54, p < .001. GNP per head (US $) also explains a significant proportion of variance in the cars per head, R2 = .80, F(1, 28) = 111.19, p < .001. The slope of the regression equation suggest that each US $ increase in GNP per head adds an average of 0.00005 cars per head.

The intercept is not meaningful as zero GNP per head implies negative value for the cars per head that is not possible. The regression equation can be used for predicting the cars per head of any developing