Economics and Quantitative Analysis Linear Regression Report

Running head: Economics and Quantitative Analysis Linear Regression Report
Economics and Quantitative Analysis Linear Regression Report
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Economics and Quantitative Analysis Linear Regression Report1
Purpose:
The report mainly aims to find the relationship between average life satisfaction and the GDP
per capita and conclude whether the relationship is significant. Linear regression analysis
which is also known as least square method is used to predict the average life satisfaction in
terms of GDP per capita and the relevance of the regression model is analyzed.
Background:
GDP stands for gross domestic product which signifies the total monetary value of all the
finished goods and services in a country within a specific period of time which is typically
one year. Thus GDP is an indicator of the size of economy of the country and change in GDP
in a year indicate the positive or negative growth of economy. Now, GDP per capita is the
monetary value allocated to each person in country on an average (Zhou and Xie 2016). Thus
higher GDP per capita indicates people in the country are in a financial good position. Now,
as financial situation of a person or family is related to their well beings, hence, it is expected
that people with good financial situation or a country with large GDP per capita have higher
average life satisfaction as per the economists.
Method:
The entire data of average GDP per capita and average life satisfaction index for 35 countries
is collected from the world health organization report in the 2017. The dataset has no missing
instances and hence the effective sample size for regression analysis is 35. Now, before
finding the association between variables the useful descriptive measures of the variables like
the average, standard deviation, minimum and maximum are produced to provide an
overview of the data. Then suitable visualizations are produced to understand the change of
the average life expectation with respect to average annual GDP per capita in the year 2017

Economics and Quantitative Analysis Linear Regression Report2
(Zuccolotto et al. 2020). Finally, the regression analysis is used to mathematically express the
average life expectation in terms of GDP per capita which is used for prediction and the
accuracy of prediction or goodness of fit of the model is presented.
Results:
As stated earlier the descriptive statistics of the two variables are calculated by using excel as
given below.
Descriptive for Average life satisfaction (Y)
Mean 6.6
Standard deviation 0.7453272
Minimum 5.2
Maximum 7.5
Descriptive for Annual GDP per capita (X)
Mean $39,011.51
Standard deviation 14006.21456
Minimum $17,122.53
Maximum $86,788.14
From the descriptive statistics it can be seen that the average life satisfaction of the countries
in the year 2017 is 6.6 with variation of satisfaction level of 0.745 among countries. The
highest satisfaction level is 7.5 corresponding to countries Denmark, Finland, Iceland,
Norway and Switzerland, whereas, the minimum satisfaction level of 5.2 corresponds to
Greece and Portugal. Similarly the average Annual GDP per capita of all the countries is
$39,011.51 and the variation of that over countries is $14006.21. The minimum GDP per