Term Project: Analyzing Life Satisfaction and GDP in OECD Countries

This project presents a quantitative analysis of the relationship between average life satisfaction and GDP per capita across 35 OECD countries. The study employs secondary data from 2017, utilizing descriptive and inferential statistics to explore this relationship. Descriptive statistics include measures of central tendency and dispersion for both variables. Inferential statistics involve a linear regression model, t-tests, and correlation analysis to test hypotheses about the association between life satisfaction and GDP. The project includes graphical representations such as histograms and scatter plots to visualize the data. Key findings include a positive correlation between life satisfaction and GDP, with specific recommendations for governmental policies to improve life satisfaction. The analysis concludes with references and a discussion of the limitations and implications of the findings. The study also includes hypothesis testing and the coefficient of determination.

A Study On average life
satisfaction and GDP per capita
in OECD

Contents of the study
 Executive Summary
Introduction
 Hypothesis
 Method
Summary Measure
 Regression Analysis
 T-test
 Scatter Plot
 Correlation Coefficient and Coefficient of determination
 Recommendation
Conclusion
References

Executive Summary
• The study analyse the satisfaction of life versus gross
domestic product in per capita among 35 selected countries
all over the world.
• In this study test and shows of some variables by descriptive
and inferential ways.
• The variables of the study are the average life satisfaction
and Gross Domestic Product (GDP) per capita.
• In the part of descriptive statistics central tendency and
dispersion has been shown.

Executive Summary
• Also in the inferential statistics linear regression model
and t-test of hypothesis has been defined.
• All the descriptive and inferential statistics are done
depends upon the average life satisfaction and Gross
Domestic Product (GDP).
• Moreover the graphical representation shows the
normality and fluctuation of the data.

Introduction
• The study analyse the satisfaction of life versus gross domestic
product in per capita among 35 selected countries all over the world.
• The study provides the satisfaction of life and gross domestic product
per capita among the selected countries (Balestra, Boarini & Tosetto,
2018).
• The main hypothesis of the study is to show the relationship between
the life satisfaction and the gross domestic product per capita.

Introduction
• From the point of purchasing power and parity a riches power of
the country is on the downward direction when the nation has a
gross domestic product fewer than 15,000 USD (Brown, Oueslati
& Silva, 2016).
• The presence of high or low gross domestic product and
satisfaction of life of a people defines the economic condition of
a country.
• The study reflect the relation between satisfaction of life and
gross domestic product by simple linear regression model.

Hypothesis
•To determine that the relationship
between average life satisfaction and
GDP.
• To test that the average life satisfaction
is not more than 7.

Method
• The study has been conducted with the help of secondary data of
135 OECD countries.
• The sample observations has been collected during the period of
2017.
• The summary statistics and the regression analysis has been shown
to determine the relationship between life satisfactions versus gross
domestic product.
• The study reflect the association between the satisfaction of life and
gross domestic product per capita among 35 selected countries.

Method
• In summary statistics the central tendency, measure of dispersion has been
illustrated (Bickel & Lehmann, 2012).
• The simple linear regression model shows the association between these
two variable and also define the correlation coefficient and coefficient of
determination among the 35 countries.
• The parameter satisfaction of life is measured on 5 to 8 scale unit. Also the
gross domestic product is defined in dollar scale.
• The general formation of simple linear regression is (Draper & Smith, 2013)
Dependent variable = Intercept + Independent variables * slope of the
independent variable.

Method
• The dependent and independent variable of the study is life
satisfaction and the gross domestic product.
• The measure of central tendency, dispersion, and all the graphical
representation ahs been done with the help of MS Excel.
• In the selection of sample among all the different countries a list of
simple random sampling technique applied.
• Firstly the secondary data has been collected.
• After that with the help of lottery method there is are 35 countries
selected and analysed.

Histogram on Average Life
satisfaction

Comment
• Figure 1 illustrate the histogram on average life
satisfaction. In the X-axis define the class and Y-axis
define the frequency.
• It is clear that the maximum of average life
satisfaction has been seen on 7 to 7.5 class.
• Similarly the minimum of average life satisfaction has
been seen on the class of 6 to 6.5.

Line Graph on Annual GDP per
capita

Comment
•Figure 2 present the line graph on annual GDP
per capita. In the X-axis represent the countries
and the Y-axis represent the frequency.
• It has been seen that the annual GDP is
fluctuated among all the countries.

Descriptive statistics of Average life satisfaction

Comment
• From the above table it has been seen that the mean
of the average life satisfaction is 6.6.
• The median or second quartile of the average life
satisfaction is 6.70.
• Similarly the mode of the average life satisfaction is
7.50.
• These are called the central tendency of the average
life satisfaction

Comment
• Again the variability of the average life satisfaction is
0.56.
• And the range of the variable is 2.30.
• Similarly the minimum and the maximum is 5.20 and
7.50.
• These are called the measure of dispersion of average
life satisfaction.

Comment
• It has been seen that the mean of the average life
satisfaction is lesser the second quartile or median.
• Therefore the average life satisfaction is negatively
skewed.
• Moreover among all the selected 35 countries lowest
life satisfaction shows the Greece and Portugal.

Summary Statistics of Annual GDP per capita

Comment
• The mean of the GDP per capita is $ 39011.513.
• The median value of these data set is $ 37843.040.
• The mode of the data does not exist, because there is
no repetition of the data.
• These are called the central tendency of the GDP per
capita.

Comment
• The standard deviation of the annual GDP per capita
is $ 14006.215.
• The minimum and the maximum of average life
satisfaction is $ 17122.53 and $ 86788.14. Hence the
range is $ 69665.610.
• These are called the measure of dispersion of the
annual GDP per capita.

Comment
• It has been seen that the mean value of the annual
GDP per capita is higher than the median.
• Therefore the skewness of the average life
satisfaction is positive.
• Moreover the highest annual GDP shows Luxembourg
and Mexico has the lowest.

Regression Analysis
The regression equation or model is as below
Average life satisfaction = 5.37 +0.00003143* Annual GDP per capita

Hypothesis test 1
Null hypothesis (H0): There is no relationship between average life satisfaction and GDP.
Alternative hypothesis (H1): There is a relationship between average life satisfaction and GDP.
Result:
• Test statistic = 17.6845
• P-value = 0.000
• Alpha = 0.05 (at 5%)
Conclusion
• It is clear that the P-Value < alpha (at either 5% or 1% level).
• Therefore null hypothesis of the test is significant.
• Thus it may be summarized that there is a relationship between average life satisfaction and
GDP.

Hypothesis Test 2 (T-test output)

Null Hypothesis: The average life satisfaction is not more than 7.
Alternative hypothesis: The average life satisfaction is more than 7.
Result:
• Test statistic = -3.2431
• P-value = 0.9987
• Alpha = 0.05 (at 5%)
Conclusion:
It has been seen that the P-value > alpha. Hence the null
hypothesis is not significant. Thus it may be summarized that the
average life satisfaction is not more than 7.

Scatter Plot

Comment:
• The scatter plot defines the relationship between the annual GDP per
capita and the average life satisfaction.
• In the X-axis represent the annual GDP per capita, and the Y-axis
represent the average life satisfaction.
• There is a positive correlation has been seen between life satisfaction
and annual GDP
• per capita.

Correlation Coefficient and Coefficient of
determination
• The coefficient of correlation between GDP and average life life
satisfaction is 0.6.
• Similarly the coefficient of determination that is the value of R-square
is 0.348.

Recommendation
• It is to recommend from the study that the government of OECD
countries has to be taken some step to develop the confident on the
population and the investors (Balestra, Boarini & Tosetto, 2018).
• In general it has been seen that the happiness of a country related to
the income. Thus this means that when the income increases, then
the happiness and growth simultaneously increases.
• The factor environment is most important according to study
because it influence the life satisfaction.

Conclusion
The following points has been concluded in the study. These are as
below
• The minimum average satisfaction of life defines Greece and
Portugal and the maximum annual GDP per capita reflect the
Luxembourg.
• The association between the average life satisfaction and
GDP per capita is positive and the coefficient of correlation
and coefficient of determination is 0.6 and 0.348.

Conclusion
• From the single sample t-test it may be summarized
that the average life satisfaction is not more than 7.
• Among all the 35 OECD countries Greece has the
lowest life satisfaction.

References and Bibliography
• Balestra, C., Boarini, R., & Tosetto, E. (2018). What matters most to people? Evidence from the OECD better life
index users’ responses. Social Indicators Research, 136(3), 907-930.
• Bickel, P. J., & Lehmann, E. L. (2012). Descriptive statistics for nonparametric models I. Introduction. In Selected
Works of EL Lehmann (pp. 465-471). Springer, Boston, MA.
• Brown, Z. S., Oueslati, W., & Silva, J. (2016). Links between urban structure and life satisfaction in a cross-section
of OECD metro areas. Ecological economics, 129, 112-121.
• Draper, N. R., & Smith, H. (2013). Applied regression analysis (Vol. 326). John Wiley & Sons.
• http://www.oecdbetterlifeindex.org
• Oja, H. (1983). Descriptive statistics for multivariate distributions. Statistics & Probability Letters, 1(6), 327-332.
• Proto, E., & Rustichini, A. (2013). A reassessment of the relationship between GDP and life satisfaction. PloS one,
8(11).
• Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons.
• Zimmerman, D. W. (2014). Comparative power of Student t test and Mann-Whitney U test for unequal sample
sizes and variances. The Journal of Experimental Education, 55(3), 171-174.

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