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Multiple Regression Model Research Paper


Interpretation of the Findings

Regressions analysis focuses on the use of models to analyze whether one or more independent variables will affect the dependent variable. Typically, regressions analysis focuses on determining whether typical quantitative data of the dependent variable will change in response to the independent variables or factors.

Multiple regressions analysis is undertaken to predict or forecast future events based on manipulating and observing one or more independent variables. The estimated benchmark is normally the work of the independent variable classified as the regressions function.

In addition, multiple regressions analysis determines if the one or more of the independent variables can be used to predict the outcome of the independent variable. In layman’s terms, regressions analysis entails determining whether there is a correlation between the independent variable and the dependent variable (Anderson, 2009).

The research qualifies a multiple regressions model. The data focuses on 20 countries burdened with general government net borrowing or net lending. The research includes one dependent variable, general government net borrowing or net lending based on data gathered from 20 countries. Theory states that the multiple regressions model includes two or more dependent variables.

The research uses five independent variables. One of the independent variables is the long term interest rates account. Another independent variable is the inflation rate account. The third independent variable is the GDP, annual growth rate account. The fourth independent variable is the economy market regulation account. Last, another independent variable is the reserve assets account (Anderson, 2009).

Table 1

Regression Statistics
Multiple R 0.823360575
R Square 0.677922636
Adjusted R Square 0.590083355
Standard Error of estimate 23.69846053
Observations 29

Regression Statistics

Applying the significant model approach, the above table indicates that the model includes 67.79 percent of the variance in determining the effect of the independent variables on the sole dependent variable, general government net borrowing or net lending.The adjusted R square can also be used to show that the 59 percent of the variance in determining the dependent variable.

In addition, the analysis of variance table clearly shows that the F value is 7.717761675 with a significance of 0.000150063. Likewise, it is important to note that the standard of error of estimate is 23.6946053 and the R square is.677922636. The variance data indicates that the F value of 7.717761675 is not < 0.0005.

Table 2

Sig.
Coefficients/Beta Standard Error t test (Stat) P-value
Intercept 23.8611012 21.21839878 1.124547684 0.272901179
General government net borrowing or net lending -2.786698962 0.886043773 -3.145103037 0.004701339
Long-term interest rates 2.109690576 3.627112824 0.581644597 0.566718833
Inflation rate -5.738402378 3.372030616 -1.701764613 0.102891175
GDP, annual growth rate -3.244896554 2.704433166 -1.1998435 0.242962356
Economy market regulation 21.44935954 12.03306601 1.782534853 0.088469208
Reserve assets* 0.000178859 3.96288E-05 4.513349795 0.000172167

Multiple Regression Data

The above data clear shows the effect of changes on the independent variables on the dependent variable, government debt. The independent variables are long term interest rates, inflation rate, GDP (annual growth rate), economy market regulation, and reserve assets.

Based on the t test alone, analysis can be done. The reserve assets account has the highest t test result at 4.513349795. This is followed close behind by the general government net borrowing or net lending account with a t test result of 3.145103037.

Third, the economy market regulation account produces a t test result of 1.782534853. Fourth, the inflation rate account generates a 1.701764613. Fifth, the long term interest rates account produces a.58164459 t test result. Lastly, the GDP, annual growth rate account shows a -1.1998435 T test result.

Based on the standard of error alone, decision making can be started. The variables have diverse standards of errors. First, economy market regulation account generates a 12.03306601 standard of error. This is followed close behind by the reserve assets account.

The account generated a standard of error amounting to 3.96288. Third, the long term interest rate account generated a standard of error amounting to 3.627112824. Fourth, the inflation rate account produces a 3.372030616 standard of error figure. Fifth, the GDP, annual growth rate generates a 2.704433166 standard of error data. Lastly, the general government generates.886043773.

Based on the coefficients factor, the variables have different coefficients. The economy market regulation has the highest coefficient or beta at 21.44935954. Next, the long term interest rates account produced 2.109690576 coefficient or beta.

The reserve assets account shows a 0.000178859coefficient or beta. The general government net borrowing or net lending account generates a coefficient or beta of -2.78669862. Further, the GDP, annual growth rate account indicates a -3.244896554.

In terms of the t test factor, the dependent and independent variables are significant. The above t test tables indicates that the regression coefficient for the general government net borrowing or net lending based on 20 countries is significantly different from zero. Further, the above t test table indicates that the regression coefficient for the long term interest rates independent variable is significantly different from zero.

Furthermore, the above t test table indicates that the regression coefficient for the inflation rate independent variable is significantly different from zero. In addition, the above t test table shows that the regression coefficient for the GDP, annual growth rate independent variable is significantly different from zero.

Also, the above t test table specifies that the regression coefficient for the economy market regulation independent variable is significantly different from zero. Lastly, the above t test table signifies that the regression coefficient for the reserve assets independent variable is significantly different from zero.

The above table indicates that each independent variable shows variance in terms of the effect of its standard deviation on the dependent variable, government debt based on 20 countries. One standard deviation in the long term interest rates independent variable produces a 2.10969 increase in the predicted government debt based on 20 countries dependent variable.

Likewise, one standard deviation in the inflation rate independent variable generates a 5.738402378 decrease in the predicted government debt based on 20 countries dependent variable. Further, one standard deviation in the GDP, annual growth rate independent variable creates a 3.244896554 decrease in the predicted government debt based on 20 countries dependent variable.

Furthermore, one standard deviation in the economy market regulation independent variable spawns a 21.44935954 increase in the predicted government debt based on 20 countries dependent variable. Lastly, one standard deviation in the reserve assets independent variable precipitates to a.000178859 increase in the predicted government debt based on 20 countries dependent variable.

Further analysis indicates that there are variances in the P value results. In terms of the government debt based on 20 countries, the computed value of P is not < 0.0005. In addition, In terms of the long term interest rates dependent variable, the computed value of P is not < 0.0005. In the area of the inflation rate dependent variable, the computed value of P is not < 0.0005.

In the environment of GDP, annual growth rate dependent variable, the computed value of P is not < 0.0005. Focusing on the area of economy market regulations dependent variable, the computed value of P is not < 0.0005. In the topic of the reserve assets dependent variable, the computed value of P is not < 0.0005.

The above table shows the relationship between the coefficient and the significance of each of the variables used in the study. The independent variable government, bonding debt composed of 20 countries, has a beta or coefficient of -2.786698962 and significance (P value) of.00470133.

In addition, the table above shows that another predictor value, economy market regulation has a coefficient or beta of 21.44935954 and a P value or significance of.08846920. Further, a third predictor value, reserve assets, generates a coefficient or Beta of.000178859 and a P value or significance of.000172167.

A fourth predictor, long term interest rates, produces a coefficient or beta of 2.109690576 and a significance or P value of.566718833. Another predictor, inflation rate, generates a coefficient or beta of -5.738402378 and a significance or P value of.102891175. A sixth predictor, GDP, annual growth rate, signifies a coefficient or beta of -3.244896554 and a significance or P value of.242962356.

Conclusion

Briefly, the findings of the research on government debt servicing are very realistic. The multiple regressions model can help in predicting the future events. The future predictability of events is based on the independent variables affecting the dependent variable. The above discussion indicates that there is a probability of a relationship between the independent variables and the lone dependent variable.

In addition, the above data clear shows strong evidences indicating the effect of changes on the independent variables on the dependent variable, government debt. The research shows that the independent variables, long term interest rates, inflation rate, GDP (annual growth rate), economy market regulation, and reserve assets influence the outcome of the dependent variable.

In addition, the t test shows that the reserve assets account has the highest t test result at 4.513349795.In terms of t test, both the dependent and the independent variables are significant.

Further, the independent variables research shows that one standard deviation has diverse effects on the standard deviation of the dependent variable, government debt based on 20 countries. One standard deviation in the long term interest rates independent variable produces a 2.10969 increase in the predicted government debt based on 20 countries dependent variable.

In addition, that there are variances in the P value results. In terms of government debt based on 20 countries, the computed value of P is not < 0.0005. In addition, In terms of the long term interest rates dependent variable, the computed value of P is not < 0.0005.

The above table shows the relationship between the coefficient and the significance of each of the variables used in the study. The independent government variable is based on debt data gathered from 20 countries. The data has a beta or coefficient of -2.786698962 and significance (P value) of.00470133.

The above brief summary of the findings indicate that the predictability of the future business transactions is bright. The government and any interested party can use the findings of the research as a basis for improving the decision -making process.

The above data clearly shows that the independent variables play important roles in increasing or decreasing the ability to predict the path of the dependent variable, general government net borrowing or net lending based on 20 countries.

The independent variables can be reorganized to institute changes in the outcome of the predictability of future business and economic events. Indeed, the independent variables have a significant effect on the outcome of the dependent variable, general government net borrowing or net lending based on 20 countries.

Reference

Anderson, D. (2009). Statistics for Business and Economics. New York: Cengage Press

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IvyPanda. (2019, December 25). Multiple Regression Model. Retrieved from https://ivypanda.com/essays/multiple-regression-model/

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"Multiple Regression Model." IvyPanda, 25 Dec. 2019, ivypanda.com/essays/multiple-regression-model/.

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IvyPanda. "Multiple Regression Model." December 25, 2019. https://ivypanda.com/essays/multiple-regression-model/.

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IvyPanda. 2019. "Multiple Regression Model." December 25, 2019. https://ivypanda.com/essays/multiple-regression-model/.

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IvyPanda. (2019) 'Multiple Regression Model'. 25 December.

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