THE CALCULATIONS HAVE TO BE MADE VIA THE SOFTWARE: R The objective of the evaluation is to carry out a data-driven examination of the economic prosperity of countries.

Why are some countries rich and others poor? We are going to study several possible drivers of economic development: countries' human capital; countries' efforts to develop new technologies; countries' business environments; and, countries' political institutions.

The file named data_week_8.csv comprises information sourced from the World Bank for 217 nations from the year 2017. Notably, the variables captured in this data file include:

• GDP: GDP per capita, PPP (constant 2017 international $); this represents the average income generated per individual in a nation for a specific year.
• LE: Life expectancy at birth (in years); this indicates human capital in terms of health and overall well-being.
• RDE: Spending on research and development (as a percentage of GDP); this highlights a nation's commitment to technological advancements.
• CoB: Expense to initiate a business (as a percentage of GNI per capita); this can be seen as a gauge for how business-friendly a country is, akin to assessing bureaucratic hurdles or "red tape."
• CPIA: CPIA score for transparency, responsibility, and public sector corruption (with 1 indicating low and 6 indicating high); this evaluates the integrity and efficiency of governmental institutions.

The analysis should address the following:

Question A:

Investigate the dependent variable (GDP per capita):

1. Calculate the descriptive statistics for the outcome variable?

2. How do you interpret the disparity between the median and mean values of the dependent variable? Plot a histogram for GDP per capita and save the graphic (ensure you add this to the answers - and do the same for any subsequent request for diagrams or tables).

3. What's the maximum value? Which nation has the top spot in the dataset in terms of wealth? How do you interpret this? What does it indicate about the income metric we've chosen?

Question B:

Analyze the explanatory variables:

1. Construct a table showing correlations between the dependent variable and all the independent variables. What's the relationship between RDE and GDP? Is this expected? 2. Observe the direct correlation between GDP and RDE. How does the relationship appear now? Can you speculate on the reasons behind it? 3. Inspect and present the correlations between each pair of explanatory variables. Which pair indicates the highest risk of collinearity?

Question C:

Based on the data given:

1. Evaluate the OLS assumptions required prior to initiating the empirical analysis. If these aren't met, detail and explain the necessary measures to address these issues.

2. Determine the most fitting econometric model for GDP per capita. Which independent variables account for a significant proportion of the variation in the dependent variable? Justify why certain independent variables are chosen for the model over others.

3. After deducing the optimal OLS model, revisit the OLS assumptions. If these post-estimation assumptions are not met, outline and explain the remedial steps.

4. What insights does the model provide regarding the factors influencing GDP per capita?

5. In the chosen econometric model for GDP per capita, identify the y-intercept. What does this value signify in practical terms? Is the y-intercept plausible in terms of representing a potential real-world scenario?

6. Are there any variables left out that should be taken into account?

7. Highlight any potential constraints or limitations in our empirical analysis that need consideration.

THE CALCULATIONS HAVE TO BE MADE VIA THE SOFTWARE: R What are the R commands used to arrive at these results? (Please send the screenshots here, it will be really helpful) While some variables are pegged to GDP and others to GNI, constraints in data accessibility prevent a consistent output/income measure in this context. However, for this analysis, you're permitted to overlook the disparities between GNI and GDP.

The analysis should address the following:

Using the given data, develop the most suitable econometric model for GDP per capita. What insights does this model offer about the factors influencing GDP per capita?

Ensure comprehensive elaboration. For instance, you might showcase familiarity with potential shortcomings of the study (stemming from the data or methodology) or display a deep understanding of the methodology by accurately decoding the findings.

Country Name	Country Code	GDP	LE	RDE	CoB	CPIA
Afghanistan	AFG	2058.400221	64.13		82.3	2
Albania	ALB	12770.97504	78.333		12
Algeria	DZA	11737.41616	76.499	0.54243	12.5
American Samoa	ASM
Andorra	AND
Angola	AGO	7310.896589	60.379		17.4
Antigua and Barbuda	ATG	19840.28404	76.752		9.1
Argentina	ARG	23597.11775	76.372	0.54152	10.4
Armenia	ARM	12115.13929	74.797	0.22788	0.9
Aruba	ABW	38897.12267	76.01
Australia	AUS	48482.64746	82.5	1.87372	0.7
Austria	AUT	54169.96399	81.64390244	3.05212	5.1
Azerbaijan	AZE	14121.40694	72.693	0.18468	1.6
Bahamas, The	BHS	36298.448	73.554		13.8
Bahrain	BHR	47709.77802	77.032		1
Bangladesh	BGD	4160.703325	72.052		22.9	2.5
Barbados	BRB	15789.04116	78.981		7.6
Belarus	BLR	18280.19892	74.12926829	0.58411	0.6
Belgium	BEL	50442.27054	81.49268293	2.70234	5.6
Belize	BLZ	7193.63431	74.365		34.6
Benin	BEN	3044.517156	61.174		3.7	3.5
Bermuda	BMU	81834.95587	81.44195122
Bhutan	BTN	11142.47467	71.129		3.9	4.5
Bolivia	BOL	8423.69756	70.945		46.6
Bosnia and Herzegovina	BIH	13753.83205	77.128	0.20048	16.1
Botswana	BWA	17253.6875	68.812		0.7
Brazil	BRA	14524.61354	75.456	1.26326	4.8
British Virgin Islands	VGB
Brunei Darussalam	BRN	60994.53156	75.585		1.1
Bulgaria	BGR	21387.27664	74.81463415	0.75239	1.2
Burkina Faso	BFA	2044.386987	60.768	0.70072	42.6	3.5
Burundi	BDI	773.5728587	60.898		33.9	1.5
Cabo Verde	CPV	6643.179892	72.57		15.4	4
Cambodia	KHM	3928.373934	69.289		51.3	2
Cameroon	CMR	3554.521539	58.511		35.8	2.5
Canada	CAN	48317.09658	81.9	1.67165	0.4
Cayman Islands	CYM	69753.08972
Central African Republic	CAF	912.8030453	52.24		154.7	2.5
Chad	TCD	1587.032328	53.712		171.3	2.5
Channel Islands	CHI		82.766
Chile	CHL	24470.70362	79.909	0.35518	5.9
China	CHN	14344.42124	76.47	2.14512	1.5
Colombia	COL	14171.3205	76.925	0.24294	14
Comoros	COM	3032.262133	63.912		84.1	2.5
Congo, Dem. Rep.	COD	1059.810762	60.026		28.6	2
Congo, Rep.	COG	4241.858343	63.954		77.7	2
Costa Rica	CRI	20347.03477	79.914	0.42336	8.6
Cote d'Ivoire	CIV	4830.750515	57.017		16.5	3
Croatia	HRV	26800.11898	77.82682927	0.8645	7.2
Cuba	CUB		78.662	0.43066
Curacao	CUW	25475.48966	78.01707317
Cyprus	CYP	38050.85774	80.672	0.56092	12.4
Czech Republic	CZE	38824.88792	78.97804878	1.79079	1
Denmark	DNK	55356.68078	81.10243902	3.046	0.2
Djibouti	DJI	4885.205039	65.893		58.8	2.5
Dominica	DMA	11304.04333			15.5	4
Dominican Republic	DOM	16735.36442	73.689		14.5
Ecuador	ECU	11617.91222	76.584		34
Egypt, Arab Rep.	EGY	11014.48648	71.656	0.67941	22.2
El Salvador	SLV	8454.055551	72.872	0.18115	41.4
Equatorial Guinea	GNQ	22550.95816	58.061		103.4
Eritrea	ERI		65.538		27	2
Estonia	EST	33856.18111	78.09268293	1.28867	1.2
Eswatini	SWZ	8408.043122	58.319		16.4
Ethiopia	ETH	2021.562908	65.872	0.27441	57.8	3
Faroe Islands	FRO		82.29512195
Fiji	FJI	13429.30062	67.252		16.9
Finland	FIN	47570.13358	81.63170732	2.7569	0.8
France	FRA	44577.06457	82.57560976	2.20557	0.7
French Polynesia	PYF		77.251
Gabon	GAB	15006.84404	65.839		7.2
Gambia, The	GMB	2072.646833	61.44		128.2	2
Georgia	GEO	13589.70739	73.414	0.29104	2.5
Germany	DEU	52952.87502	80.99268293	3.03763	6.8
Ghana	GHA	4983.688856	63.463		17.5	3.5
Gibraltar	GIB
Greece	GRC	28645.04362	81.28780488	1.13109	1.6
Greenland	GRL		70.62243902
Grenada	GRD	16217.49985	72.388		15.3	4
Guam	GUM		79.631
Guatemala	GTM	8322.21681	73.81	28	22.9
Guinea	GIN	2417.803795	60.706		62.6	2.5
Guinea-Bissau	GNB	1925.237737	57.673		99.9	1.5
Guyana	GUY	12005.4051	69.624		9.8	3
Haiti	HTI	2980.960584	63.29		200.2	2.5
Honduras	HND	5561.994542	74.898	0.04	39.7	3
Hong Kong SAR, China	HKG	59849.24818	84.6804878	0.79925	1.1
Hungary	HUN	29465.12624	75.81707317	1.3486	5.4
Iceland	ISL	55638.49206	82.66097561	2.10444	1.8
India	IND	6182.922109	69.165	0.66584	15
Indonesia	IDN	10935.63172	71.282	0.2381	11
Iran, Islamic Rep.	IRN	14535.87358	76.271	0.83027	1.4
Iraq	IRQ	10719.01222	70.294	0.04455	43.3
Ireland	IRL	78655.62619	82.15609756		0.2
Isle of Man	IMN
Israel	ISR	38833.97343	82.55121951	4.81602	3.2
Italy	ITA	41581.12079	82.94634146	1.37744	14.1
Jamaica	JAM	9600.104746	74.267		4.8
Japan	JPN	40966.59322	84.0997561	3.21254	7.5
Jordan	JOR	10003.57498	74.292		24.2
Kazakhstan	KAZ	24862.96612	72.95	0.12972	0.3
Kenya	KEN	4046.234771	65.909		26.3	3
Kiribati	KIR	2235.694985	67.851		40.2	3.5
Korea, Dem. People's Rep.	PRK		71.91
Korea, Rep.	KOR	40957.41806	82.62682927	4.55324	14.6
Kosovo	XKX	10530.48166	71.99512195		1	3
Kuwait	KWT	50855.55286	75.311	0.08118	1.7
Kyrgyz Republic	KGZ	5046.691535	71.2	0.10707	2.1	3
Lao PDR	LAO	7257.812107	67.277		7	2.5
Latvia	LVA	28650.00776	74.62926829	0.51012	1.7
Lebanon	LBN	15987.65434	78.833		42
Lesotho	LSO	2668.291425	52.947		7.7	3
Liberia	LBR	1515.64454	63.295		15.7	3
Libya	LBY	13238.00054	72.52		30.3
Liechtenstein	LIE		83.74634146
Lithuania	LTU	33761.87124	75.4804878	0.89808	0.6
Luxembourg	LUX	112308.1738	82.09512195	1.30327	1.7
Macao SAR, China	MAC	126183.6761	83.989	0.1715
Madagascar	MDG	1584.424475	66.311	0.01465	35.7	2.5
Malawi	MWI	1446.473062	63.279		44.6	2.5
Malaysia	MYS	26661.50742	75.828		12.7
Maldives	MDV	18058.58108	78.325		4.7	2.5
Mali	MLI	2246.797421	58.452	0.29175	58.4	3
Malta	MLT	42644.05085	82.34634146	0.58275	7.4
Marshall Islands	MHL	3706.253158			11.9	3.5
Mauritania	MRT	5077.185987	64.464		19.3	3
Mauritius	MUS	21415.11611	74.51463415	0.36636	1
Mexico	MEX	19721.26098	74.947	0.32829	17
Micronesia, Fed. Sts.	FSM	3490.130487	67.618		141.7	3.5
Moldova	MDA	11651.31745	71.717	0.25374	5.6	2.5
Monaco	MCO
Mongolia	MNG	11311.76113	69.509	0.13475	1.4	3.5
Montenegro	MNE	19682.28335	76.667	0.34898	1.5
Morocco	MAR	7312.056779	76.218		8
Mozambique	MOZ	1283.65987	59.309		92.9	2.5
Myanmar	MMR	4739.915445	66.558	0.03186	40.1	2.5
Namibia	NAM	10171.42149	63.021		11.4
Nauru	NRU	12975.39238
Nepal	NPL	3565.197807	70.169		27.4	3
Netherlands	NLD	55088.6338	81.76097561	1.98308	4.4
New Caledonia	NCL		77.53902439
New Zealand	NZL	42285.3199	81.65853659	1.3657	0.3
Nicaragua	NIC	6004.02858	74.068		65.4	2.5
Niger	NER	1163.687784	61.599		8.3	3
Nigeria	NGA	5190.356127	53.95		29.2	3
North Macedonia	MKD	15649.92742	75.589	0.35525	3.4
Northern Mariana Islands	MNP
Norway	NOR	64050.75617	82.6097561	2.09343	0.9
Oman	OMN	29077.3636	77.393	0.2276	3.2
Pakistan	PAK	4571.205078	66.947	0.23597	7.9	3
Palau	PLW	17826.39361			2.9
Panama	PAN	30446.83723	78.149	0.14699	6.5
Papua New Guinea	PNG	4285.628756	64.01		20.3	3
Paraguay	PRY	12590.54844	73.992	0.14883	58.2
Peru	PER	12506.52938	76.286	0.12101	7.5
Philippines	PHL	8120.868793	70.952		19.7
Poland	POL	30064.50348	77.75365854	1.03445	12
Portugal	PRT	33044.71674	81.42439024	1.32833	2.1
Puerto Rico	PRI	34363.74599	79.63453659		0.8
Qatar	QAT	91738.75279	79.981		6.3
Romania	ROU	27141.91933	75.3097561	0.5039	0.4
Russian Federation	RUS	25926.44385	72.45146341	1.10656	1.1
Rwanda	RWA	1975.248661	68.341		44.6	3.5
Samoa	WSM	6480.845407	73.046		7.3	4
San Marino	SMR	58867.01336			9.1
Sao Tome and Principe	STP	3952.945394	69.933		13.2	3.5
Saudi Arabia	SAU	47306.22232	74.874		6.8
Senegal	SEN	3203.906368	67.38		33.8	3.5
Serbia	SRB	16611.01686	75.53902439	0.87353	2.3
Seychelles	SYC	27242.65603	74.3		13.2
Sierra Leone	SLE	1642.60747	53.895		10.3	3
Singapore	SGP	95350.43567	83.09512195	1.94431	0.5
Sint Maarten (Dutch part)	SXM	37914.12297
Slovak Republic	SVK	30077.84553	77.16585366	0.88267	1.1
Slovenia	SVN	36505.67722	81.02926829	1.86581	0
Solomon Islands	SLB	2663.939508	72.645		28.9	3
Somalia	SOM	867.4542909	56.709		203.6	1.5
South Africa	ZAF	12701.34893	63.538	0.83215	0.2
South Sudan	SSD		57.365		305	1.5
Spain	ESP	39528.92539	83.28292683	1.2058	4.1
Sri Lanka	LKA	12584.10479	76.648		10.4
St. Kitts and Nevis	KNA	25360.09102			7.2
St. Lucia	LCA	14945.73328	75.907		21.5	4.5
St. Martin (French part)	MAF		79.62195122
St. Vincent and the Grenadines	VCT	12245.12634	72.3		15.8	4
Sudan	SDN	4327.782553	64.881		27.8	1.5
Suriname	SUR	18283.68839	71.463		94
Sweden	SWE	51947.95425	82.4097561	3.39676	0.5
Switzerland	CHE	69103.57026	83.55121951	3.37286	2.3
Syrian Arab Republic	SYR		70.967		7.9
Tajikistan	TJK	3252.932655	70.647	0.11536	19.3	2.5
Tanzania	TZA	2530.603317	64.479		48.1	3
Thailand	THA	17422.95235	76.683	1.00403	6.2
Timor-Leste	TLS	3145.483121	69.007		0.5	2.5
Togo	TGO	2012.240693	60.489		66	3
Tonga	TON	6467.124278	70.701		7.1	3.5
Trinidad and Tobago	TTO	26342.61666	73.245	0.09204	0.8
Tunisia	TUN	10605.29488	76.31		4.6
Turkey	TUR	27913.81872	77.161	0.96105	15.9
Turkmenistan	TKM	14205.02651	67.956
Turks and Caicos Islands	TCA	27060.58966
Tuvalu	TUV	3896.748473				3.5
Uganda	UGA	2074.652436	62.516		42.6	2
Ukraine	UKR	11871.12362	71.78097561	0.44839	0.8
United Arab Emirates	ARE	67183.60531	77.647		13.4
United Kingdom	GBR	45744.71049	81.25609756	1.69829	0
United States	USA	60109.65573	78.53902439	2.81741	1.1
Uruguay	URY	23009.87423	77.632	0.48393	22.5
Uzbekistan	UZB	6518.804687	71.388	0.15566	3.1	2
Vanuatu	VUT	3081.835198	70.172		44.4	3
Venezuela, RB	VEN		72.246		352.2
Vietnam	VNM	7155.443187	75.241	0.52674	6.5
Virgin Islands (U.S.)	VIR		79.36829268
West Bank and Gaza	PSE	6401.740891	73.74		45
Yemen, Rep.	YEM		66.086		73.5	1.5
Zambia	ZMB	3485.02178	63.043		34.2	3
Zimbabwe	ZWE	3274.611198	60.812		110	2

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