## Application of Correlation Analysis

Correlation analysis articles are helpful in providing an alternative on how a certain business problem can be solved. The application of correlation analysis involves the use of a regression model that generates a quantitative analysis of the study. The use of regression analysis models in the article by Gholamrezapour and Gang (2018) entitled “Impact of monetary policy on the U.S. economy” helps understand the importance and relevance in the analysis of various arrangement settings in the advancement of development over time across the time country. Assuming the monetary yield is declining or only holding consistent, most organizations can not expand their benefits. Thus, inflation is seen as a positive premise when it helps support shopper interest and utilization, driving financial development.

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## Summary of the Problem Being Researched

The article tackles the effect of monetary policy on the economic growth of the U.S. The economy of any nation depends on the flow of money in the economic system. Monetary policy instruments and laws influence economic growth. The economic growth of the U.S. is affected by the inflation rate, interest rate, and exchange rates. The economic disturbance is largely influenced by decisions regarding monetary policy factors (Gholamrezapour & Gang, 2018). The authors try to solve the existing business problem in the economy in the context of monetary policies. The authors utilized regression analysis by regressing monetary variables with respect to economic growth.

## The Use of Regression Analysis in the Study

Effective application of regression models helps in recommending how a certain problem can be solved based on the variables. The model involves the aspect of correlation analysis which shows how different variables correlate with each other. The variables are quantitatively measured using data from reliable sources to identify the numerical relationship between the dependent and independent variables. The author has used a linear regression model to show how monetary instruments influence the U.S. GDP.

The linear regression model is formulated from the dependent and independent variables. The relationship between GDP and Monetary policy instruments is presented by the statistical tool SPSS, which is used to analyze quantitative data. Based on the analysis, the independent variables comprise the rate of inflation, interest, and exchange rate. On the other hand, the main dependent variable is the gross domestic product of the U.S.

For effective analysis, the researcher is obligated to collect data from a reliable source. The regression model was formulated as *Y=* *β0+ β1X1+ β2 X2+β3X3. *Y represents the U.S. economic growth measured in GDP, X1 interest rate, X2 exchange rate, and X3 inflation rate. *β0 *shows a constant, and *β1 *–* β3 *are the regression coefficients (Gholamrezapour & Gang, 2018). The regression coefficients are determined by analyzing the standard error and beta of each variable for the study.

## Potential Application of the Business Problem

The relationship between monetary policies and economic growth is real. The economic growth of many countries depends on the flow of money in the economy. The existing relationship between the two means that the government, through the central bank, has to regulate the interest rates and inflation rates. The exchange rate can be managed through the balance of payment surplus. Through the concept of regression analysis and models, economists can evaluate different variables with the economic growth of a given nation. Since there is a negative relationship between interest rate and GDP, the economy might be experiencing problems such as an excess supply of money. The GDP figures, as answered to financial backers, are now adapted to inflation. A review of the interest helps in minimizing the borrowing rate, hence stabilizing the economy.

## Reference

Gholamrezapour, A., & Gang, Z. (2018). The impact of monetary policy on economic growth in America’s economy using a new approach tvp-favar. *Amazonia Investiga*, *7*(15), 58-68. Web.

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