Posted: August 7th, 2013

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SOM306/Operations Management

From the data provided, is seems there is correlation between the increase in money supply and inflation. It shows that as money supply increases there is an increase in the inflation related to the increase. However, the correlation is not consistent all through. There are some dispersions and bigger variances between the variables at some points. The coefficient on the money supply variable suggests that for every single growth within five years in the money supply, the value of inflation increases by 0.567.

To forecast the inflation when the money growth is at 8, the equation of the correlation has to be defined using the b_{0 }and b_{1}. b_{0}= 0.136, which is the value of Y when the value of X is 0. The equation is

Y= b_{0} + b_{1}(X)

At a money growth rate of 8, the inflation is Y = 0.136 + 0.567×8

=4.8

Therefore, the value of inflation when the money growth is at 8 is 4.8.

The “significance F” or P value is at 0.1084. The ANOVA table provides F statistics that place the claim that no significant relationship exists between the variables. Therefore, the bigger the “significance F” at the last column in the ANOVA table is, the more the claim that no significant relationship exists between the variables holds true. It is recommended that a strong relationship exists when P value, which is the same as the significance F is less than 0.05. At this value, it suggests the correlation is 95%. When it is greater than 0.1, as in our case, it means there is a lot of dispersion or variance between the variables, and the correlation is at 90% or slightly lower. Therefore, this shows that the relationship might be present but not quite strong. Thus, the P value for this case indicates that a strong relationship between the two variables does not exist. The regression coefficient is only slightly significant.

The coefficient of determination or R-Square determines the proportion of the variation between the dependent variables that are explained by independent variables. It can also be used as a measure of how well a prediction can be made out of the values. In a linear regression, which is our case, the coefficient of determination should range from 0 to 1. The higher it is the better. Thus, the coefficient of determination in this case suggests that not all the dependent variables are explained by the independent variables since at 0.3 it is too closer to 0. Therefore, not all the increases in the growth of money can explain the rate of inflation. This suggests there were other reasons affecting the rate of inflation.

From an interpretation of the coefficient of determination, it can be concluded that faster growth of money is not always associated with higher rates of inflation. The analysis shows there is a relationship between the two variables, suggesting that an increase in growth of supply of money contributes to higher inflation but other factors may exists since not all the dependent variables can be explained by the independent variables. It means there are other variables that exist to explain variables not explained by increase in money supply. From the data itself and the scatter diagram, it is evident that in some cases, the rate of inflation decreased while the supply of money increased. For instance, in 1995 the growth of money was 2.8 while the inflation was at 3.8. In 2000, the growth of money was higher at 7.1, while the inflation rate stood at 3. This showed that the inflation decreased while the supply of money increased.

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