ECONOMETRICS PROJECT

ECONOMETRICS PROJECT

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DISCUSSION AND FINDINGS

RESULTS OF ANALYSIS

Considering the research objectives, our analysis of data will in benchmark provide an overall overview of what was found in the project. The concept of happiness linked up with the economic growth in the UK was a major concern. Economic growth was measured by the Gross Domestic Product which is abbreviated as GDP. Life data on average in the UK satisfaction of life, population, and Real Gross Domestic Product was used. Use of multivariate analysis using regression was applied to bring about the relationship of how happiness and Economic Growth in the United Kingdom are correlated.

The findings and discussion part are essentially the heart of every project paper. It uses systematic overview and outlines all the data necessary used during the data analysis section. At the same time in this section, we shall link between our research problem and the exact empirical finding in order to show the relationship of the project. We shall use statistical tests such as hypothesis testing, correlation expounding, just but to give an example. At last, a conclusion shall be discussed in order to give a whole summary of what has been done in the project and to be precise in chapter five, which entails the findings and conclusions of data analysis.

The data to be used is on satisfaction in the UK Kingdom, the population trends, and the real growth in gross Domestic product. These figures are given from the year 1973 up to 1996. The computed figures which have been presented in an Excel Spreadsheet will be used to investigate the relationship of satisfaction level, the population trend and real GDP. Our endogenous variables in this case are the Satisfaction level while the exogenous variables are the Population trends and the Real GDP. To explain further, Endogenous variables are the variables which are explained within the model while exogenous ones are the ones which tell us the trend in the model. They are already determined within the model and hence, they are used to give more information about the endogenous variable, hence the name explanatory variables.

We shall run a regression model in Excel sheet and command the computer to do a regression for this study. We shall also come up with a linear regression line model that will be defined by some given variables with the coefficients of variation clearly indicated. For example, we can come up a model in the form of Y= B0+B1 X 1+B 2X 2+e

Where Y is the variable to be determined and is called Endogenous variable. In our case, the endogenous variable is the concept of happiness,

B0 is the intercept after correlation and links up the endogenous variable with other explanatory variables.

B1 is a measure of correlation as a result of a unit change in the Endogenous variable. It measures the rate of change of Concept of happiness to a change in population trend, B 2 measures a unit change in coefficient X2 with respect to a unit change in Y, and lastly e is the error term.

FINDINGS

REGRESSION STATISTICS Multiple R 0.330544601 R Square 0.109259733 Adjusted R Square 0.020185706 Standard Error 0.632180288 Observations 23 ANOVA   dfSS MS F Significance F Regression 2 0.980439831 0.49022 1.226617 0.314420501 Residual 20 7.993038343 0.399652 Total 22 8.973478174         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Current Satisfaction 36.1844071 34.05293744 1.062593 0.300629 -34.84877567 107.2175899 -34.848776 107.2175899 Population -0.00065576 0.000648069 -1.01187 0.323684 -0.002007613 0.000696085 -0.0020076 0.000696085 Economic Growth(GDP) 0.000316545 0.000233072 1.358141 0.189544 -0.000169635 0.000802724 -0.0001696 0.000802724 DISCUSSION

From the above findings of the regression model, we can deduce a linear regression line of the form; Y= B0+B1 X 1+B 2X 2 and this reduces to

Satisfaction= 36.1844071-0.00065576 X 1+0.00316545 X 2

(34.05293744) (0.000648069) (0.000233072)

Hypothesis Testing

A hypothesis is a guess which is supposed to be either proved right or wrong. We develop two variables here. The first one is a null hypothesis represented as H0 and the other called alternative hypothesis represented as H1. The aim of hypothesis testing is to determine whether a given exogenous variable is significant in the model. We say that if T-calculated > 0, then we reject the null hypothesis and conclude that that variable is statistically significant.

The overall model will be tested at 95% confidence level as shown in the data above. The hypothesis testing process is as follows;

Test for intercept (B0)

H0: B0= 0

H1: B0≠ 0

T-cal = (36.1844071-0)/ 34.05293744= 1.062593. This means that the value is significant since T-cal> zero at 2 degrees of freedom with the (P-value =0.300629). The upper and lower values are stated as 107.2175899 and -34.84877567 respectively.

Test for (B1)

H0: B1= 0

H1: B1≠ 0

Hence, to test for B1;

T-cal= (-0.00065576-0)/ 0.000648069= -1.01187. This means that B1 is not statistically significant in this model at 2 degrees of freedom with the (P-value =0.323684). The upper and lower interval values are 0.000696085 and -0.002007613 respectively.

Test for (B2)

H0: B2= 0

H1: B2≠ 0

Thus T-cal = (0.00316545-0)/ 0.000233072= 1.358141. This also implies that B2 is statistically significant in this model at 2 degrees of freedom with a (P-value =0.189544). The upper and lower interval values are 0.000696085 and -0.000169635 respectively

Overall significance of the model

The overall model significance can be tested as follows to determine whether the variables are significant or not. Despite that some variable in this case population was found not to be significant, we can testify whether in the model, it is significant or not as follows;

H0: B0= B1= B2= 0

H1: B0=B1= B2≠ 0

From the figure shown in the above analysis, F= 0.314420501. The model is significant overall and thus all the variables are said to be significant at 95% confidence interval with 2 degrees of freedom.

Sum of squared residuals = 7.993038343

Unadjusted R2 = 0.109259733

Adjusted R2 = 0.020185706

F-statistic (2, 23) = 1.226617 (p-value = 0.314420501)

Interpretation

The unadjusted R2 is 10.9260% and the adjusted R2 is 2.0186%. This adjusted R2 is very small indeed statistically. It tests whether all the variables have been included within the model. It thus means from the model that, only 2.0186% of all the variations are the only ones that are explained within the model and the rest 97.9834% of all the variations are explained by other variables not stated in the model.

Conclusion

A conclusion is a reflection of what has been done so far. It will give a clear and brief summary of the findings in a critical manner. It will also state what is likely to be done so that to improve the research done. To start with, on the things that have been done; a research was carried out to determine the relationship between concept satisfaction of people in the United Kingdom and the Economic growth (GDP). The other variable under consideration was the population trends. It is good to note that current satisfaction, economic growth is all significant at 95% confident interval with 2 degrees of freedom. The only variable which is not statistically significant is the population growth. This means that if the population increases by unity, the level of satisfaction will reduce by the margin given above.

The overall model is very significant at 95%confident interval and hence is best used to describe the research objective or the questions.

Excel spreadsheet was good software in doing the analysis and it can be used so long as one is familiar with it and knows what to regress against. The only problem which can occur when using it is the ability to distinguish between the endogenous and exogenous variables and then regress them. Other methods which can be applied to analyze such kind of data is use of SPSS software, Stata, and other recommended social or statistical software’s which requires knowledge to apply them.

The main objective was to determine the extent to which the satisfaction levels in the people of UK is influenced by factors named above such as Economic growth and the population trends in this county, United Kingdom. As economists we use these findings to come up with a decision on how the economy will be run. In this case, if a nation needs to grow, it should embark on boosting economic growth rates (GDP) levels other than increasing in population without changing the satisfaction levels. The opposite is also true in that, a nation can improve in population levels followed by an improvement in economic levels. The trend will be positive and hence statistically helpful.

Statistically hypothesis testing is very crucial since many of the variations are better explained in this analysis. It is also easy to comprehend in that if one test has been calculated, one compares it with the T-statistic and then concludes whether it is significant or not. For instance, a researcher after doing research of regression output, we have the values of T-values which are produced automatically. These values will be compared with the T-calculated ones to determine their applicability. If the calculated ones are more than zero, then the value under consideration is statistically important, and if it is less, then it is not significant. In this case population which is represented by B 1 in our analysis is the only exogenous variable that was not significant in this model.