Kareem Saheed

CHAPTER FOUR 4.0 DATA PRESENTATION AND DATA ANALYSIS 4.1 Introduction This section comprises presentation and analysis of data, the data used for this project work is a secondary data, extracted from CBN statistical bulletin, Nigeria GDP historical data, and Niarametrics dashboard released in 2021. The variables present are used to carry out a multiple regression analysis to know which independent variable explains or have a significant impact on the dependent variable. Table 4.0: Presentation of data YEAR GDP($)Billion GEXP(N)Billion FDE($)Million NBGD(N)Trillion- - - - - - - LINE GRAPH SHOWING THE INCREAMENT OR DECREAMENT IN THE RGDP, Fig 4.0: RGDP against Year. 4.2 PRESENTATION AND INTERPRETATION OF RESULTS This section comprises of analysis and presentation of the result in this study using the methodology discussed in the previous chapter. Model specification; + Y = The Real GDP ($) Billion. in (N)Billion. ($) Million. (N)Trillion. Table 4.1: Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Rising Budget, Fiscal deficit, Government Expenditureb . Enter a. Dependent Variable: Gross Domestic Product b. All requested variables entered. 4.3Fitting the regression model using OLS: Table 4.2: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant- 1.530 .266 Government Expenditure .034 - .024 Fiscal deficit -.011 .003 -.700 -3.492 .073 Rising Budget - -1.428 -4.007 .057 a. Dependent Variable: Gross Domestic Product The fitted regression Model using OLS. (4.1) 4.3TEST FOR MODEL ADEQUACY The following tests are used to check if the ordinary least square regression model applied is adequate and which variable(s) contributes to the adequacy of the model using the below Table 4.3: Anova table. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression- .037b Residual- Total- a. Dependent Variable: Gross Domestic Product b. Predictors: (Constant), Rising Budget, Fiscal deficit, Government Expenditure Test for model significance: Hypothesis: H1 = Decision Rule: Reject the null hypothesis if p-value < α – value (0.05), otherwise do not reject the null hypothesis. Decision: Since = 0.037 < 0.05, then we reject the null hypothesis. Interpretation: We can conclude based on the available data that at least one of the regressors (independent variables) is significant i.e., it has impact on the dependent variable (Real GDP) at α = 0.05 level of significance. We can see that at least one independent variable contributes to the response variable, thus the regression model is said to be adequate. Table 4.4: Summary table Model R R Square Adjusted R Square Std. Error of the Estimate 1 .988a .975 - a. Predictors: (Constant), Rising Budget, Fiscal deficit, Government Expenditure b. Dependent Variable: Gross Domestic Product. Using the R2 value above, 98.9% of the variation in the dependent variable (Real GDP) is explained by the independent variables (Rising Budget, Fiscal deficit, Government Expenditure). Thus, the model is adequate. Now to test which variable(s) contributes to the dependent variable (Real GDP), we test each variable individually for the significance of each parameter. 4.4Individual t-test for the parameters: Hypothesis: Table 4.5: T- test table for individual parameters. Variables T p.value B Constant 1.530 - in (N)Billion. () 6.281 .024 .034 ($) Million. () -3.492 .073 -.011 (N)Trillion. () -4.007 .057 -25.449 Decision rule: Reject Ho if tcal > ttab, or if p-value < α - value, Conclusion: Result from the test for significance above reveals that only in (X1) contributes significantly to the fitted model. (X2), (X3) are not significative. they do not contribute significantly to the model. 4.5INTERPRETATION OF THE FITTED MODEL The fitted regression Model using OLS. Findings The fitted model reveals that at constant, if the Real Gross Domestic Product (RGDP) then for every unit increase in the country’s GDP, there will be 0.34 (N)Billion increment in the government spending/expenditure. Also, for every unit increase in the country’s GDP, there will be a ($) Million decrement in the country’s Also, for every unit increase in the country’s GDP, there will be a ($) decrease Explanation The result implies that, if there is positive relationship between the RGDP and the Government spending, i.e Government spending contributes positively to the Nigeria real GDP, and for every unit increase in ($) Billion in the RGDP, then there has been a 0.34 (N)Billion increment in the government spending/expenditure. Also, there is negative relationship between the RGDP and the i.e. the contributes negatively to the Nigeria real GDP, and for every unit increase in ($) Billion in the RGDP, then there has been a ($) decrement in the country’s . Also, there is negative relationship between the RGDP and the country’s i.e., the contributes negatively to the Nigeria real GDP, and for every unit increase in ($) Billion in the RGDP, then there has been a ($) decrease CHAPTER FIVE 5.0SUMMARY, CONCLUSION AND RECOMMENDATION 5.1. SUMMARY This project work is to ascertain the relationship between the public sector social management and the economic growth of Nigeria. The statistical tool used in this project work is multiple regression. From the test for model adequacy, based on the available data, it was discovered that at least one of the regressor in (X1) is significant i.e., has impact on the dependent variable (RGDP) at α = 0.05 level of significance Fitting the model using all the independent variables we find out that the adjusted R2 of the model is 0.939, R = 0.988 showing a strong correlation between the dependent variable (RGDP) and the independent variables. And the R2 = 0.975 showing that the linear regression explains 98.90% of the variance in the data, or 97.50% of the variation in the dependent variable is explained by the independent variable. Individual tests for the independent variables reveal that only variables contributes significantly to the model, the other independent variables do not contribute significantly to the model. This means that, if there is positive relationship between the RGDP and the Government spending, i.e Government spending contributes positively to the Nigeria real GDP, and for every unit increase in ($) Billion in the RGDP, then there has been a 0.34 (N)Billion increment in the government spending/expenditure. The regression equation using OLS is given by: 5.1. CONCLUSION This study has illustrated that: 1. There is a positive relationship between the government spending on infrastructures and the Gross Domestic product. (GDP). 2. The Fiscal deficit have a negative effect on the RGDP. 3. The national budget does not have a positive impact on the RGDP.