Analysis of factors affecting inflation rate in Rwanda (1990-2009)

4.2.2 Regression equation of the model

1. Summary of output from SPSS regression analysis of multivariate

Variables	Coefficients	t	P-Value
Constant	0.430	0.267	0.793
Money Supply (M2)	1.706	3.981	0.001
Real GDP (GDP)	-1.476	-3.603	0.003
Exchange Rate (ER)	0.297	1.092	0.292
Lending Rate (LR)	0.733	1.192	0.252
R = 0.972 Confidence intervals = 95% F= 64.470 R Squared = 0.945 Model significance = 0.000

Examining the output from this regression analysis; we look to the p-value of the F-test to see if the overall model is significant. With a p-value of zero to three decimal places, the model is statistically significant. The R-squared is 0.945, meaning that approximately 94.5% of the variability of CPI is accounted for by the variables in the model. In this case, the adjusted R-squared indicates that about 94.5% of the variability of CPI is accounted for by the model; even after taking into account the number of predictor variables in the model. As explained below, the coefficients for each of the variables indicates the amount of change one could expect in CPI given a one-unit change in the value of that variable, given that all other variables in the model are held constant.

In order to arrive at a more efficient model, variables with low t-values or incorrect signs in the over-parameterized regression were excluded.

2. Specification of general model

CPI = 0.43 M₂^1.706 GDP^-1.476 ì

Log CPI= 2.7 +1.706 log M₂ +1.476 log GDP+ ì

3. Predictive accuracy of the general model

One way to measure the overall predictive accuracy of a multiple regression model is the R-square value. The interpretation of R-square is: "The amount of variance in the dependent variable that can be explained by the model." For our model the R-square value is 0.945, this means the model explains 94.5% of the variance and so the model will produce perfect predictive accuracy. The point is, the closer to 1.0 the R-square value is, the better the model. The closer the R-square value is to 0, the worse the model.