LogarithmiC ModeliNg

Logarithmic Modeling

The logarithmic equation that models the data is found out that is

Y1 = 7411.9ln(x) + 13698

R² = 0.7633

Based on this model, the graph has been constituted that is given below;

By utilizing the data model, I have identified the value 4 years after the starting year utilizing Y1, the answer that has been calculated are as follows;

Y1 = 7411.9ln(4) + 13698

Y1 = 23973.08

On the basis of above caculation it has been noted that the value of Y1 does not fit in the real situation. Since there, is the slight difference in comparision with the 4th year amount that has been calculated since starting time. It was calculated around $22,191.14 while, the value of amount that we calculated by utilizing the model is $23973.08. Since the value is getting higher we can say that there is lack in the prediction.

10 years after the starting year:

Y1 = 7411.9ln(10) + 13698

Y1 = $30764.53

By utilizing the model of logrithim, one can say that the amount of compensation is predicting at very high rate that seems to be not feasible in the real time situations. So, in my opinion the Y1 doesnot fit with the data of compensation.

On the basis of above data we can see that there is decay logarithmically in the compensation that is ranging from third to sixth year, then we have seen that there is huge decay in the 10th year.

It seems that the data is growing logarithmically except in about 2010 there was a severe decay and in 2020 there will be slight decay from $39758 to $38951.61

In the empirical analyses below we also applied the variable HUMRES which stands for expenditures in education, training and R&D called human resources in the categorization of the EC. According to the theoretical framework outlined in the previous chapter, technological change depends to a large extent on local/regional factors of innovation Logarithmic Modeling. Thus the unit of empirical investigation applying equation (3.1) should be some sub-national geographical entity. Since the lowest level of spatial aggregation of the type of data we need for analysis is the county the selected unit of analysis is Hungarian counties. The spatial unit is denoted by i while t stands for time in equation (3.1).

(3.1)TFPGRi,t = a0 + a1KNATt + a2RDi,t + a3KIMPi,t + a4INFRAi,t + a5EDUi,t + ei,t,

where

TFPGR is the annual rate of growth of Total Factor Productivity at the county level,

KNAT is domestically ...