Description
All are attached below, max word count is 9 pages
Problem Description:
The role of temperature in economic development has been an important question. It is often documented that hot countries (or countries closer to equator) tend to be poor. In recent decades, with the warming at a global scale, scientists and policy makers are increasingly concerned about the economic consequence of warmer and drier weather.
In this assignment, you are given historical data on temperature, precipitation, and economic outcomes for a sample of 106 countries.You are required to analyse the impact of temperature, precipitation on income levels (measured by per capita GDP in natural logarithm), and more important, on income growth (measured by annual growth of per capita GDP).
Each question is described below (2 + 3 + 4 + 6 + 8 + 4 + 1+ 4 = 32 marks; professional report = 8 marks):
Locate the data file (IndividualBusStats.xls) on CANVAS.
- Has the global been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset.
- Calculate the sample covariance and correlation for the two relationships in question 2 above using Data Analysis Tool Pack or Excel statistical functions. In addition, you are required to calculate the sample covariance and correlation using a second method (using basic Excel formulae without Data Analysis Tool Pack). The calculations by the second method should be carefully laid out in Excel and should NOT use any hard-wired Excel statistical functions e.g. COVARIANCE.S, CORREL, et al. You can use the Excel sort command, the sum command, and any other non-statistical excel commands. Carefully interpret your results.
- Use simple regression to explore the relationship between (i) annual growth of per capita GDP over 1990-2000 (Y) and mean temperature over 1990-2000 (X); (ii) annual growth of per capita GDP over 1990-2000 (Y) and mean precipitation over 1990-2000 (X), respectively. You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equations, then carry out any relevant two-tailed hypothesis tests using the critical value approach at the 5% significance level. Carefully interpret your hypothesis test results.
- Now use multiple regression to explore the relationship of annual growth of per capita GDP over 1990-2000 (Y) with, mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equation, then interpret the estimated coefficients on the mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). Carry out any relevant two-tailed hypothesis tests using the p-value approach at the 5% significance level, and an overall significance test using the p-value approach. Carefully interpret your hypothesis test results.
- Write down a paragraph or two to illustrate: a) why the hypothesis test is required in the above regression analysis (either simple or multiple); b) the underlying mechanism/intuition of a two-tailed hypothesis test in the context of regression analysis. Aid your illustration with diagrams that you think appropriate.
- Carefully interpret the adjusted R-squared in the multiple regression analysis.
- If you could request additional data to study the factors that influence the annual growth of per capita GDP, what extra variables would you request? Illustrate two such variables. Carefully explain why you choose these two variables (by drawing evidence from the literature such as journal articles, newspapers, et al), types of your proposed variables (e.g. numerical or categorical), and how each of your proposed variables will be measured in the regression model.
2.Do hot and dry countries tend to be poor (with lower per capita GDP)? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present two relationships. Carefully interpret and explain.
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Explanation & Answer
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Data sources:
The dataset consists of a sample of 106 countries.
The historical weather data are taken from the Terrestrial Air Temperature and Precipitation: 1900–2006 Gridded Monthly Tim
Data on the level and annual growth of per capita GDP are drawn from the World Development Indicators (WDI).
variables:
per_cap_GDP2000
growth1990_2000
tem1950_1960
pre1950_1960
tem1990_2000
pre1990_2000
per capita GDP in year 2000 (US$), expressed in natural logarithm
annual growth of per capita GDP over 1990-2000, expressed in percentage (%)
mean temperature over 1950-1960 (measured in degrees Celsius per year)
mean precipation over 1950-1960 (measuerd in units of 100 mm per year)
mean temperature over 1990-2000 (measured in degrees Celsius per year)
mean precipation over 1990-2000 (measuerd in units of 100 mm per year)
0–2006 Gridded Monthly Time Series, Version 1.01 (Matsuuraand Willmott 2007).
country
Nigeria
Panama
United Arab Emirates
Spain
Austria
Canada
Vanuatu
Australia
Sierra Leone
Brunei
Ghana
Fiji
Egypt
Turkey
Belgium
Cote d'Ivoire
Iran
India
Suriname
Senegal
Togo
Rwanda
Peru
Mauritius
Philippines
Mali
Germany
Chile
Oman
Honduras
Argentina
Uganda
France
Lesotho
Laos
Haiti
Paraguay
Costa Rica
Mauritania
United States
Botswana
Brazil
Trinidad and Tobago
Korea
Ireland
per_cap_GDP2000 growth1990_2000 tem1950_1960 pre1950_1960
6,341999468
0,017646708
26,52577
13,88756
8,309018174
3,343584973
24,68316
28,04099
10,41305497
0,351553339
26,84328
1,631635
9,596491202
2,487687259
13,48391
6,651904
10,10905589
2,300388065
7,549002
9,950752
10,09370492
0,582056958
4,841475
9,364386
7,2934069
1,483790792
24,15511
27,481
9,984110761
2,135885605
15,86288
9,355159
4,932303899
-2,469349752
25,70401
28,29842
9,798824364
-0,428450843
27,20606
30,216
5,554783345
1,456127111
26,31561
14,2106
7,638472188
1,673783596
23,89741
31,08469
7,279647225
2,512322923
21,03608
0,3591493
8,375048385
2,602163169
12,03383
5,847577
10,04505372
2,029001451
9,626793
7,704759
6,478979435
-1,23001107
26,24332
15,25539
7,420584576
2,423024302
13,79623
3,894315
6,094278759
3,585117687
25,23642
12,74809
7,607024507
-1,087064319
26,61093
22,14153
6,404656503
0,055498592
26,9184
9,227755
5,711110209
-0,37610378
26,07221
12,79692
5,563669533
1,252000313
19,12939
10,76035
7,578446198
1,279218094
13,26135
8,056942
8,276159434
4,281374685
24,04545
16,54273
6,978035781
0,530769128
25,38977
24,72692
5,597944577
0,967916696
27,92813
8,587226
10,07052326
1,841511218
8,429167
7,853553
8,532062419
4,485345735
11,73466
6,047864
9,05966537
1,877228526
25,61425
1,243683
6,985144126
0,306134294
23,70358
15,80122
8,950027132
2,272210933
16,78562
8,500537
5,56784437
3,261433402
22,20274
11,86919
10,01520912
1,726098494
10,32313
7,900137
6,07876169
2,485163739
12,33063
7,941034
5,784400396
3,828208018
23,5511
18,59604
6,698926252
-1,597174052
23,80118
15,15912
7,416742365
0,217254068
22,6103
13,86094
8,235591267
2,253617492
21,69974
37,24714
6,517032455
-0,871080318
28,07941
3,883507
10,50053423
2,055704447
13,06245
9,107277
8,166871928
2,465008425
20,15629
5,036779
8,229445317
0,469748127
21,38649
13,77444
8,769532711
4,960545686
26,27126
20,02921
9,413851957
6,467544334
11,18752
11,73753
10,17509225
6,376785862
9,004849
10,22423
Colombia
Indonesia
Belize
Algeria
Cuba
Uruguay
Cabo Verde
Norway
Ecuador
Denmark
Dominican Republic
Nicaragua
Sri Lanka
China
Japan
Gambia, The
Guinea-Bissau
Hungary
Guatemala
United Kingdom
Nepal
Jordan
Samoa
Venezuela
Bolivia
Iceland
Greece
New Zealand
Cameroon
Italy
Burkina Faso
Guinea
Sweden
Morocco
Madagascar
Netherlands
Gabon
Kenya
Comoros
Switzerland
Congo, Dem. Rep.
Jamaica
Kuwait
Benin
Mexico
Niger
7,832205071
6,659537737
8,120984986
7,475918546
7,91830057
8,835650025
7,138330075
10,54879496
7,276057886
10,33343542
7,961755713
6,915228712
6,768144319
6,866279408
10,5592454
6,387134578
5,730567593
8,43907632
7,417159268
10,24529802
5,435860707
7,409513382
7,340878832
8,485090821
6,905334812
10,37405551
9,396235014
9,520842773
6,476959817
9,907857589
5,540155202
5,895731095
10,29637093
7,196643912
5,682242675
10,17158195
8,32499502
5,985151307
6,4705188
10,54187024
6,004420883
8,127019037
9,822298024
6,237152507
8,875959976
5,284935254
1,012220862
2,747891283
3,752138039
-0,164740644
-1,786452329
2,5076454
7,958015454
2,958794983
0,011939686
2,201612231
3,15293866
1,10224032
4,438963593
8,655843283
1,356308293
-0,034229888
-0,437058263
1,899129864
1,460227831
2,050550258
2,508794623
0,486085514
1,171336005
0,422032291
1,75394906
1,648187168
1,596620334
1,404064547
-2,15358778
1,671002216
1,986588691
1,245459329
1,620467187
1,514975138
-1,187089028
2,743167206
-0,533867964
-0,940087436
-0,405154735
0,728696009
-8,474498001
1,126682665
-2,867917947
1,702369936
1,928195582
-2,038653695
21,26738
25,50005
25,42072
16,80155
25,31185
16,99368
23,36591
3,29523
19,90497
7,6489
24,47031
24,78792
26,605
13,42423
12,96264
25,8047
26,46866
10,43776
19,84146
9,041043
20,44924
18,24715
26,06212
24,99508
18,62292
2,96588
14,95799
11,98894
24,23516
11,81764
27,41963
25,03104
5,282156
17,04815
20,67844
9,666837
24,77283
20,31933
24,80303
4,975078
24,07286
23,81385
25,5101
27,02115
18,61772
27,78783
20,56923
24,98619
23,68984
5,642025
13,27607
11,09725
7,898182
9,886539
13,73285
6,644507
17,84686
16,98472
23,26587
9,934293
17,9546
13,73073
18,698
6,082292
22,9342
8,130628
15,23243
2,726088
30,906
11,94165
10,56473
8,354614
7,117124
12,29694
20,22872
10,35821
9,59704
22,86032
6,289688
4,353302
15,35144
7,447047
22,07469
11,0526
22,98245
13,79524
14,23098
19,84624
0,939177
11,75305
9,262138
5,96913
Malawi
Romania
Central African Republic
Tanzania
Chad
Bhutan
Syrian Arab Republic
Luxembourg
Tunisia
Mozambique
Poland
El Salvador
Burundi
Saudi Arabia
Cambodia
5,052325511
7,414517241
5,526276812
6,018477217
5,110558976
6,576731138
7,071258601
10,79417316
7,701574067
5,766317693
8,41215575
7,601671169
4,91606063
9,123837609
5,712336331
1,968176058
-0,771896213
-1,60894537
0,462086634
-1,433425935
4,340952013
2,494926336
3,626749501
3,286240995
2,790436047
3,769896707
2,334291939
-3,022205969
1,22463352
-0,93...