Angel whether there are differences between countriesAngel whether there are differences between countries

Angel HeMs. Hoult MDM4U1-01January 15, 2018Differences in HappinessStatistical Analysis: Formal Report Abstract: The purpose of this report was to identify differences from one country to another that related to the topic of happiness. After investigation, the data showed that the average level of happiness varied from one country to another. Other differences showed that the countries who invested more into areas that had a strong correlation to happiness such as education and health, had happier people. Lastly, human freedom had only a moderate correlation to the average happiness score, but when further investigating had been conducted, it showed various aspects of freedom has different correlations to happiness.  In conclusion, the data gathered does show that there are differences between countries regarding happiness. This is shown through differences in happiness scores, and external factors that differ between countries such as government spending and freedom.Introduction: Over the pass decades, efforts have been put into the improvement of quality of life. Equal rights movements attempt to create a more harmonious society, while other scientific discoveries in medical care or technological advances helps reduce many difficulties, ultimately, people work hard to live a happier life. This study compares 19 countries around the world, to answer the focus question of whether there are differences between countries that relate to the topic of happiness.  The motivation behind this study is to discover variables to focus on in order to become happier on a personal level such as moving to a country with the variables or a larger level such as how the government could make its citizens happier.  Reviews in previous studies has been conducted before making of this report. In “The Cause of Happiness and Misery” section of the World happiness report (Report) the external determinants include: Income, work, community, governance, values and religion. And personal determinants listed: mental health, physical health, family, education, gender, and age (Easterlin, 2).  Following the lead of the report, statistical data that was related to income, community, governance, and health had been gathered. Many previous studies that have related happiness to income have been previously done like in the journal “Will raising the incomes of all increase the happiness of all? (Journal)”, which is why this report did not focus on the income of the people between countries. The area of study this report would fall under is global studies, as it will compare statistics between countries to draw a conclusion.  The following is a table of terms and definition to assist in further understanding.GDP A monetary value of all the goods and services produced in country in a period (typically a year).Education ExpenditureThe amount of money spent on public education.Health ExpenditureThe amount of money spent to promote, restore, or maintain health. Methods: The sample in this report was chosen systematically, with every eighth country in the World Happiness Report being chosen.  The variables used in the analysis and its definition will be in the following table. Average Happiness ScoreThe mean value of the happiness scores from 0 to 10 in a given country. School Life ExpectancyThe expected total number years of schooling a person is expected to receive.  Life Expectancy The expected total number years a person is expected to live.  Education Expenditures per CapitaEducation expenditure expressed as a dollar value then divided by the country’s population for an estimate of the government’s investment in education for each person. Health Expenditures per CapitaHealth expenditure expressed as a dollar value then divided by the country’s population for an estimate of the government’s investment in education for each person. Human Freedom ScoreAn average of the person freedom score and the economic freedom score of  a country, scored out of ten.Personal Freedom ScoreA measure of the degree that people can enjoy major freedoms (civil liberties)— such as freedom of speech, religion, and association and assembly. Economic Freedom Score A measure of the degree of control people have over their own labor and property.The plan is to compare average happiness scores between countries using a bar graph and see if there is a difference between happiness scores. The mean score will be calculated as well as standard deviation to determine how much the happiness score vary between countries. Furthermore, there will be scatter plot graphs and lines of best fit used to show correlation between variables to then derive an analysis from. Google spreadsheets will be used to draw the graphs and lines of best fit.  It will also be used to calculate the standard deviation and mean values. Graphs and Analysis: One Variable Analysis- Country VS Average Happiness ScoreThe one variable data is presented in a bar graph, the x axis lists the 19 countries chosen and the y- axis has their corresponding average happiness scores. The graph visually depicts that the happiness level from one country differs from another. Using google spreadsheets, the mean happiness score of this sample turned out to be 5.303 and the standard deviation being 1.107. The fact that there are values that differ from the mean such as New Zealand with a 7.3 indicates that there is a difference in happiness between countries. The standard deviation indicates that there is a noticeable spread in the data, considering the range is only out of ten and the standard deviation is just a little over one whole point. Two Variable Analysis- Government SpendingThis section will look at two aspects, government spending on education and health and how well they correlate to happiness. They are graphed using scatter plot graphs.When comparing school life expectancy to average happiness scores, the r score of 0.81 shows that there is a strong positive linear correlation between the two variables. As the x values increase usually the corresponding y value will increase as well. One can then draw the conclusion that more education is related to more happiness. The following graph will determine how government spending on education ties into this.To compare how education expenditures correlate to happiness, the correlation between happiness and education was first established. This scatter plot then shows that the amount of money invested into education does impact the school life expectancy of the citizens of that country.  The r score of this correlation is 0.765 which qualifies as a strong relation. There is a positive linear relationship implying that by spending more on education, school life expectancy should increase, consequently increasing happiness as well. Although majority of the points seems to cluster near the left side of the graph, the points do still support the trend on the graph. The two figures above show that when the government spends more money on factors that are correlated to happiness, the people become happier. Another aspect of government spending is in health. Using life expectancy as an indicator of health, the correlation between health and happiness produced an r score of 0.66 which does qualify as moderate to strong. This is then reflected in the second scatter plot, as the correlation between the government spending on health and the average happiness score is 0.86. The logarithmic nature of the line indicates that as the health expenditures increase the average happiness score should also increase. Looking at both health and education expenditures, the data shows that there is a positive correlation between the expenditures and happiness scores, when the expenditures are used on variables strongly related to happiness. And that governments who spend more on those variables have happier citizens than those who do not. Two Variable Analysis- FreedomWhen mentioning happiness, freedom is one of the variables that comes to mind rather quickly, the following graph depicts average happiness scores of a country and its human freedom score.  For this analysis, average happiness was made to be dependent on human freedom. What was interesting about this graph was that the correlation was not as strong as one would expect it to be. The overall trend does show that freer countries are usually happier but with an r score of 0.646 there is only moderate correlation between the two variables. Freedom is then further broken down into personal freedom and economic freedom to determine any patterns. Both graphs have a positive linear correlation, however the data points on the graph for personal freedom was just a bit more scattered. The correlation between happiness and economic freedom was turned out to be strong with an r score of 0.69 and the r score between happiness and personal freedom was moderate at 0.63. So, while happiness does increase as freedom increases, the data shows that economic freedom is more important to the happiness of people in a given country than personal freedom. And that countries who have more economic freedom tend to be happier. ConclusionAfter analysis of the data gathered, there are in fact happiness related variables that vary from one country to another. The main differences are as follows: how happy the people of a country are, the amount that the government spends services that relate to happiness, as well as how much freedom -specifically economic freedom- a country has. Discussions: There were a few limitations to the conclusion. For example, using life expectancy may not have been a good enough indicator of the healthiness of a country and more representative data could not be gathered either due to restricted access or the data could not be found. Furthermore, the data gathered was not as raw as it could have been, as many of the variables were calculated beforehand like freedom scores. And to calculate those values by hand is beyond capabilities. The data gathered came from several research organizations and raw data was gathered from the World CIA Factbook. The methodology the indices used were reviewed before usage, and all sources were transparent with their values and calculations. However there may be bias present in the calculations favouring one factor over another. There would be a larger concern if the average happiness score was solely a self-reported score as there would be the possibility of a response bias, however the happiness score is obtained through a mathematical calculation mitigating that possibility. The data gathered should not be biased or too inaccurate as they are not sponsored by any particular country. As for the purpose of this report, the sample was gathered systematically to reduce any bias. The graphs and trends drawn should be accurate with minimal to no bias, therefore the evidence throughout the report should be strong. Lastly, for any future studies that uses the same focus questions, there should be data gathered less obvious factors to find new discoveries. For example, if weather and geography attribute to the happiness of the people in a country. Also have a solid action plan ahead of time, because majority of the data gathered were not or could not be used. Another recommendation is to try and find more raw data, using precalculated scores from indices may still raise question on its validity as the origin of the value needs to be searched for. Reference List Contact CIA. (2016, April 01). Retrieved January 12, 2018, from, R. A. (1995). Journal of Economic Behavior and Organization (Vol. 27, p. 2, Rep.). Los Angeles: Elsevier Science. Retrieved November 24, 2017, from, J., Layard, R., & Sachs, J. (Eds.). (2012). World happiness report (pp. 59-60, Rep.).  New York: The Earth Institute. Retrieved November 24 , 2017, from, J. F., Layard, R., & Sachs, J. D. (Eds.). (2017). World Happiness Report (Rep.). Retrieved December 8, 2017, from Sustainable Development Solutions Network website:ásquez, I., & Por?nik, T. (2016). The Human Freedom Index 2016 (Rep.). Retrieved January 12, 2018, from Cato Institute website: Education Expenditures per Capita and Health Expenditures per Capita: The original statistic was presented as a percentage of the country’s GDP, however GDP varies between countries so the monetary value invested would differ. Education and Health Expenditure (as a percentage of GDP) was multiplied by its respective GDP to get a dollar value. However, GDP may differ between due to population, so while a country may spend more on education it may be necessary for the larger population. So the dollar value was then divided by the population to get an approximate dollar value of expenditures for each citizen. The calculation is modeled by: Expenditure (as a percentage of GDP) x GDP Official Exchange Rate PopulationThe following values were calculated using Google Spreadsheets, here are its corresponding formulas. Mean Formula: Standard Deviation Formula: Line of Best Fit- A and B values: Correlation Coefficient Formula: Google Spreadsheets provided a variance value (the square of r), square root r^2 for the actual r score.  Data Tables