Introduction
The effectiveness of the field of statistics is increasing day by day, as it has the tendency and ability to articulate the things in an effective manner. Statisticians used the power of this field to predict and anticipate the future in an organized manner (Montgomery, Peck & Vining, 2007). This field has number of techniques and models from which relationship among two variables can be analyzed along with predicting the future capabilities accordingly. The tools and techniques used in the field of statistics, used specifically for modeling and forecasting. The analysis initiated through the statistical measures usually has a dependent variable and an independent variable. It is required to select a dependent and independent variable in this analysis and then apply the relevant statistical techniques accordingly and effectively. It is required to have T-Statistics, R-Square and Regression Modeling along with Correlation Analysis.
The dependent variable would be = Interest Rate
Independent Variable would be = GDP Growth rate
Analytical Framework
For the economic development of an economy, both interest rate and GDP Growth rate has its effectiveness. Most of the times, it is observed that both of these variables moved in different directions with each other. Changing in the prevailing interest rate of an economy is one of the core businesses of the Central Bank of the country. The interest rate and GDP growth rate of the United States have been selected for the same analysis, from the period 2005 to 2014.
The aforementioned graph and table is showing that both interest rate and the GDP growth rate of the United States (US) were fluctuated heavily. It shows that both of these elements have a direct linkage with each other. GDP Growth rate has envisaged negative growth of the economy as well.
Regression Modeling & Correlation of Coefficient
Regression analysis is a statistical process and measure used for estimating the core relationship among different variables. Most of the times, these variables are dependent variables and independent variables. Most of the time, it is used for analyzing the major relationship among two of the variables and for predicting the future as well. Apart from the regression equation, there is yet another important tool which has been used for the same analysis is Correlation Coefficient. Correlation Coefficient is a measure of strength to analyze the core direction of two different variables. It usually analyzes the relationship among two different variables. Most of the times, it is essential for analyzing the relationship among securities and stock performances. The function of regression is mentioned below
Y = A + BX
Interest Rate = Intercept of (Interest Rate) + Slope of (GDP Growth rate)
Statistical Packages for Social Sciences (SPSS) has used in this analysis to present the regression modeling test and result
T-Statistics
A T-Test is a statistical hypothesis in which the statistics follows a distribution of T, if the null hypothesis is supported. It is used specifically to analyze how much two data sets are different from each other or the data set follows a normal distribution or not. It is required to apply the T-Test Statistics on the selected data of interest rate and GDP Growth rate and following result is found
Conclusion
References
Montgomery, D., Peck, E., & Vining, G. (2007). Student solutions manual to accompany introduction to linear regression analysis (1st ed.). Hoboken (N.J.): Wiley-Interscience.
United States Financial Indicators (2013), [Online], retrieved from < http://www.tradingeconomics.com/united-states/indicators> Accessed on 2014-Oct-02nd
