1 ECONOMICS AND QUANTITATIVE ANALYSIS LINEAR REGRESSION REPORT DUE DATE: 27 May 2019 WORD LIMIT: 1200 words WEIGHTING: 40% Instructions As an economist working in the OECD you have been asked to...

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please make the report by using the data attached a 2017 data and please follow the instructions i dont want to be fail please i really need to score well


1 ECONOMICS AND QUANTITATIVE ANALYSIS LINEAR REGRESSION REPORT DUE DATE: 27 May 2019 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 brief literature review on the association between life satisfaction and GDP is required. 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 10,000). (3 marks).  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, Ireland, and Norway appear to be outliers in terms of GDP per capita. Re- estimate your regression model without Luxembourg, Ireland, and Norway. How does this affect the slope coefficient and goodness of fit? Explain. (5 marks). 5. Discussion (5 marks) In this section, provide a brief 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 DayMay 21, 2021ECO82001Southern Cross University

Answer To: 1 ECONOMICS AND QUANTITATIVE ANALYSIS LINEAR REGRESSION REPORT DUE DATE: 27 May 2019 WORD LIMIT:...

Shakeel answered on May 23 2021
141 Votes
data 2017
    Country    Average life satisfication    Annual GDP per capita
    Greece    5.2    $24,076.69                Average life satisfaction    Annual GDP per capital
    Po
rtugal    5.2    $28,106.39
    Hungary    5.3    $25,817.20
    Turkey    5.5    $24,915.17            Mean    6.59    $39,011.51
    Estonia    5.6    $28,429.82            Std. Dev    0.75    $14,006.21
    Slovenia    5.8    $30,388.25            Minimum    5.20    $17,122.53
    Italy    5.9    $34,178.53            Maximum    7.50    $86,788.14
    Japan    5.9    $38,195.72
    Korea    5.9    $35,968.09
    Latvia    5.9    $24,092.42
    Poland    6.0    $26,129.21
    Slovak Republic    6.1    $29,901.86
    France    6.4    $37,843.04
    Spain    6.4    $33,696.31
    Czech Republic    6.6    $31,798.03
    Mexico    6.6    $17,122.53
    Chile    6.7    $20,815.21
    United Kingdom    6.7    $39,331.89
    Belgium    6.9    $41,788.98
    Luxembourg    6.9    $86,788.14
    United States    6.9    $53,219.42        SUMMARY OUTPUT
    Austria    7.0    $44,109.21
    Germany    7.0    $44,066.19        Regression Statistics
    Ireland    7.0    $66,362.55        Multiple R    0.5906891715
    Israel    7.2    $32,442.54        R Square    0.3489136974
    Australia    7.3    $46,248.03        Adjusted R Square    0.3291838094
    Canada    7.3    $43,274.12        Standard Error    0.6104480728
    New Zealand    7.3    $34,851.66        Observations    35
    Sweden    7.3    $45,208.56
    Netherlands    7.4    $47,973.21        ANOVA
    Denmark    7.5    $46,180.44            df    SS    MS    F    Significance F
    Finland    7.5    $39,671.81        Regression    1    6.5900825368    6.5900825368    17.6845250269    0.0001871916
    Iceland    7.5    $46,911.26        Residual    33    12.2973460346    0.3726468495
    Norway    7.5    $60,396.24        Total    34    18.8874285714
    Switzerland    7.5    $55,104.24
                        Coefficients    Standard Error    t...
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