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1 ECO82001 – ECONOMICS AND QUANTITATIVE ANALYSIS (ONLINE) LINEAR REGRESSION REPORT DUE DATE: 9am Monday, 5 February 2018 WORD LIMIT: 1200 words WEIGHTING: 40% Instructions As an economist working in...

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ECO82001 – ECONOMICS AND QUANTITATIVE ANALYSIS (ONLINE)
LINEAR REGRESSION REPORT
DUE DATE: 9am Monday, 5 Fe
uary 2018
WORD LIMIT: 1200 words
WEIGHTING: 40%

Instructions
As an economist working in the OECD you have been asked to prepare a short report that examines
the statistical association between average life satisfaction and GDP per capita using the data
contained in the spreadsheet (linear regression assignment data).
Your report needs to be structured as follow:
1. Purpose (2 marks)
In this section, the purpose of the report needs to be clearly and concisely stated.
2. Background (4 marks)
In this section, a
ief literature review on the association between life satisfaction and GDP is
equired. Why are economists interested in this particular issue?
3. Method (4 marks)
In this section, the data source and empirical approach used to examine the relationship between life
satisfaction and GDP needs to be detailed.
4. Results (20 marks)
In this section, you need to present and summarize the results from your statistical analysis. In
particular, the results section must:
 Provide a descriptive analysis of the two variables (e.g., mean, standard deviation, minimum
and maximum). Which countries have the lowest and average life satisfaction scores? Which
countries have the lowest and highest GDPs per capita? (2 marks).
 Develop a scatter diagram with GDP per capita as the independent variable. What does the
scatter diagram indicate about the relationship between the two variables? (3 marks).
 Develop and estimate a regression equation that can be used to predict average life
satisfaction given GDP per capita. (2 marks).
 State the estimated regression equation and interpret the meaning of the slope coefficient (to
make the interpretation easier multiply the estimated coefficient by 10, XXXXXXXXXXmarks).
 Is there a statistically significant association between GDP per capita and average life
satisfaction? What is your conclusion? (2 marks).
 Did the regression equation provide a good fit? Explain. (3 marks).
 Luxembourg is a major outlier in terms of GDP per capita. Re-estimate your regression
model with Luxembourg excluded. How does dropping Luxembourg affect the slope
coefficient and goodness of fit? Explain. (5 marks).
5. Discussion (5 marks)
In this section, provide a
ief overview of the results. What are the key strengths and limitations of
this analysis? (e.g., data, method, etc.). How do the results from this analysis compare with other
studies? (e.g., are the findings consistent?). Do these findings have clear policy implications?
6. Recommendations (5 marks).
In this section, you should present three to five well-considered recommendations.
Please ensure that your report is submitted as a single file.
Answered Same DayFeb 17, 2020ECO82001Southern Cross University

Solution

Pooja answered on Feb 18 2020
99 Votes
Table of Contents
Purpose    2
Background    3
Method    4
Results    5
Discussion    8
Recommendations    9
Appendix    10
Data:    10
Model 1:    10
Model 2:    11
References    13
Purpose
I want to predict average life satisfaction on the basis of GDP per capita.  My dependent variable is average life satisfaction. And the independent variable is GDP per capita. I use the technique of co
elation and regression analysis to know the strength of linear relationship and built a significant model. 
Background
In time-series analysis, researcher Easterlin reported that there is no significant relationship between happiness and aggregate income. According to his paradox, life satisfaction increases with GDP in poor country. In richer countries, this relation is approximately flat.
Method
I have collected my data from World Bank for variables namely average life satisfaction and GDP per capita.My population consists of average life satisfaction and GDP per capita for all the countries of the world.  I select a random sample of size 35 using the technique of simple random sampling. 
I used the technique of regression and co
elation analysis to predict the average life satisfaction on the basis of GDP per capita. I check the significance of model with the help of f test statistic and the value of coefficient of determination.
Results
The table of descriptive statistics for life satisfaction and GDP is given below
    
    Average life satisfication
    Annual GDP per capita
    
    
    
    Mean
    6.565714286
    41353.88885
    Standard E
o
    0.125887963
    2709.554046
    Median
    6.6
    38400.96643
    Mode
    7.3
    #N/A
    Standard Deviation
    0.74476323
    16029.93791
    Sample Variance
    0.554672269
    256958909.5
    Kurtosis
    -1.190079547
    4.918301073
    Skewness
    -0.253936007
    1.688870845
    Range
    2.5
    84237.13688
    Minimum
    5.1
    17894.21584
    Maximum
    7.6
    102131.3527
    Sum
    229.8
    1447386.11
    Count
    35
    35
The mean average life satisfaction is 6.565714286 with a standard deviation of 0.7447. And the mean average annual GDP per capita is 41353.88 with a standard deviation of 16029.9. The low value of standard deviation in life satisfaction indicates that its mean is reliable.  But the high value of the measure of dispersion of GDP indicates that its mean is not reliable. The distribution of GDP is cute to the right indicating that there are very few countries with the high value of GDP. The country Switzerland and Norway have the highest life satisfaction with 7.6. But the country Portugal has the lowest life satisfaction with the value of 5.1. The country Luxembourg have the highest mean GDP per capita with 102131.35. But the country Mexico has the lowest mean GDP per capita with the value of 17894.21.
The scatterplot between GDP per capita and life satisfaction is given below.
An upward trend in the scatter plot indicates that there is a positive linear relationship between life satisfaction and GDP per capita. Points of moderately close to each other indicate that this relationship is moderate. Hence I can say that there is a moderate positive linear relationship between life satisfaction and GDP per capita. That it has value of GDP per capita increases, the value of life satisfaction also increases considerably.
The regression equation is given by: y = 12187*x – 38665
The coefficient of slope tell speed change dependent variable with a unit increase in independent variable.  I can say that with one in season life satisfaction...
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