Description

BUSINESS STATISTICS 1

Assessment 3: Individual Assignment

Instructions:

This is an individual assignment with a total of 40 marks. The allocation of marks is as follows:

Report Structure

The response must be provided in the form of a professional report with no more than 10 pages (excluding the cover page).

The structure of your professional report must include:

• A Title, 

• An Executive Summary, 

• An Introduction, 

• Analysis, and 

• Conclusions. 

Submission

You must submit an electronic copy of your assignment on Canvas. See the attached Template of your submission for more details.

Excel file

This assignment requires the use of Microsoft Excel.  You will also be using Data Analysis Tool-Pack to complete the assignment.

You need to submit the Excel file along with your report. The excel file needs to be clear and carefully organized and must show all workings underlying the Professional report and associated statistical analysis. It will be treated as an appendix to your report, i.e., not included in the page count.

DO NOT leave your responses in the Excel workbook. You will need to take the relevant results from your Excel workbook and incorporate into your report. The report needs to be standalone.

Presentation Instructions:

Your written professional report should comply with the following presentation standards:

1. Typed using a standard professional font type (e.g. Times Roman), 12-point font size.
2. 1.5-line spacing, numbered pages, and clear use of titles and section headings.
3. Delivered as a Word (.doc or .docx) or PDF (.pdf) file.
4. Checked for spelling, typographical and grammatical errors. Where relevant, round to 3 decimal places.
5. With all relevant tables and charts, the report should be no more than 10 pages long.

Problem Description:

This is a further analysis of Carbon Dioxide (CO2) emissions with an objective to investigate the factors that influence carbon emissions. Governments worldwide have been promoting the use of renewable energy as a strategy not only to increase access to electricity but also to reduce environmental degradation because of its lower level of carbon dioxide emissions.  To formulate effective policies to mitigate carbon emissions, it is crucial to understand the factors that influence it.

Since this is an additional analysis on CO2 emissions, the content in the Introduction section of your report may overlap with the one in the Group Assignment. However, you are encouraged to develop/source new background materials that align with the objective of the current analysis. You will use a similar dataset as in Assignment 2 but with some additional information. The data are drawn from the World Bank database for year 2015. The sample used for analysis comprises of 57 high-income countries (HIC) and 130 low- or middle-income countries (LMIC), as per the World Bank classification of world economies. The dataset contains the following information:

1. CO2: per capita carbon dioxide emissions (metric tons)
2. group: the dummy variable group = 1 if it is a high-income country (HIC), and = 0 if it is low- or middle-income country (LMIC).
3. urban: the dummy variable urban = 1 if the country is highly urbanised (i.e 70% or more of the population live in urban areas), and = 0 if the country is not highly urbanised
4. dense: the dummy variable dense = 1 if the country is densely populated (i.e more than 200 individuals live in 1 square kilometre of land area), and = 0 if it is not densely populated.
5. GDP: per capita GDP, in USD1000, at constant 2015 prices
6. renewable: share of renewable energy consumption out of total energy consumption (in %)

Key instructions to follow:

1. Locate the data file (IndividualAssignmentData.xls) on CANVAS.
2. Use the 5% significance level where relevant or not specifically mentioned.
3. While explaining your findings, please ensure that you explain both statistical findings and provide interpretations in the context of the scenario.

[Marks distribution: 32 marks for statistical analysis + 8 marks for presentation]

1. Before estimating the regression equation, using the full sample conduct a preliminary analysis of the relationship between CO2 emissions and each of the following variables: 1) renewable; 2) GDP; 3) urban; and 4) dense. Use tables and/or appropriate graphs for the categorical variables (urban, dense) and the continuous variables (GDP, renewable). Interpret your findings by answering the following questions: Are CO2 emissions higher in urbanised countries? Are CO2 emissions higher in densely populated countries? What kind of relationship do you observe between CO2 emissions and per capita GDP? What kind of relationship do you observe between CO2 emissions and renewable energy consumption?

(4 marks)

1. Use a simple linear regression to estimate the relationship between CO2 emissions (Y) and renewable (X) (Model A). You may use the Data Analysis Tool Pack. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope coefficient. Carry out any relevant two-tailed hypothesis test of the slope coefficient using the critical value approach, at the 5% significance level, showing the step-by-step workings/diagram in your report. Interpret your hypothesis test results.

(5 marks)

1. Now use a multiple regression model to explore the relationship of carbon emissions (Y) with, renewable (X1), GDP (X2), urban (X3) and dense (X4) (Model B). You may use Data Analysis Tool Pack for this. Based on the Excel regression output, first write down the estimated regression equation and interpret the slope coefficients. Carry out any relevant two-tailed hypothesis tests for each individual slope coefficient using the p-value approach, at the 5% significance level. Carry out an overall significance test using the p-value approach. Carefully interpret your hypothesis test results.

(10 marks)

1. Compare the coefficient of renewable in Model A with the coefficient of renewable in Model B (where a number of other variables have been included) and discuss why they are different?  Determine which one is a better model and why, explaining carefully your decision in the context of the scenario.

(4 marks)

1. Estimate Model B separately for 1) HICs and 2) LMICs. Briefly discuss how the results are different across HICs and LMICs.

(4 marks)

1. Using your results in Q5, predict CO2 emissions for:
   1. A densely and highly urbanised HIC with 20% of renewable energy out of total energy consumption, and a per capita GDP of USD 40,000;
   2. A densely and highly urbanised LMIC with 20% of renewable energy out of total energy consumption, and a per capita GDP of USD 4,000.

(2 marks)

1. If you could request additional data to study the factors that influence carbon dioxide emissions, what extra variables would you consider? Pick two such variables and discuss 1) the rationale for including them in the regression model and 2) their expected relationship with CO2 emissions (i.e direction of relationship) [You could draw evidence from journal articles, newspapers, etc]

(3 marks)

References:

Dogan E. & Seker, F. (2016a). An investigation on the determinants of carbon emissions for OECD countries: Empirical evidence from panel models robust to heterogeneity and cross-sectional dependence. Environmental Science and Pollution Research, 23, 14646-14655.

Dogan E. & Seker, F. (2016b). Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy. Renewable Energy, 94, 429-439.

Jebli, M. B., Farhani, S., & Guesmi, K. (2020). Renewable energy, CO2 emissions and value added: Empirical evidence from countries with different income levels. Structural Change and Economic Dynamics, 53, 402-410.

Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: Evidence from macro-level, cross-country analyses. Population and Environment, 35, 286-304.

Sharma, S. (2011). Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Applied Energy, 88(1), 376-382.