1. Has the globe been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset.
2. Do hot countries tend to be poor (with lower per capita GDP)? Do dry countries tend to be poor? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present
two
relationships. Carefully interpret and explain.
Notes 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 Time Series, Version 1.01 (Matsuura and Willmott 2007). Data on the level and annual growth of per capita GDP are drawn from the World Development Indicators (WDI). variables: per_cap_GDP2000per capita GDP in year 2000 (US$), expressed in natural logarithm growth1990_2000annual growth of per capita GDP over 1990-2000, expressed in percentage (%) tem1950_1960mean temperature over 1950-1960 (measured in degrees Celsius per year) pre1950_1960mean precipation over 1950-1960 (measuerd in units of 100 mm per year) tem1990_2000mean temperature over 1990-2000 (measured in degrees Celsius per year) pre1990_2000mean precipation over 1990-2000 (measuerd in units of 100 mm per year) Data countryper_cap_GDP2000growth1990_2000tem1950_1960pre1950_1960tem1990_2000pre1990_2000 Nigeria6.34199946760.017646708226.5257713.8875626.8517213.39307 Panama8.30901817383.343584973324.6831628.0409925.1147827.48667 United Arab Emirates10.41305496610.351553338826.843281.63163526.914331.263214 Spain9.59649120192.48768725913.483916.65190414.411496.297207 Austria10.10905589242.30038806467.5490029.9507527.9520929.221741 Canada10.09370492250.58205695844.8414759.3643865.2961489.385453 Vanuatu7.29340690011.483790791724.1551127.48124.6238625.28607 Australia9.98411076132.135885605215.862889.35515915.809897.964385 Sierra Leone4.9323038986-2.469349751925.7040128.2984226.1997325.13283 Brunei9.7988243637-0.428450842627.2060630.21627.0674226.36298 Ghana5.55478334521.456127110926.3156114.210626.6784112.75037 Fiji7.63847218761.673783596223.8974131.0846924.0320725.13651 Egypt7.27964722452.512322923421.036080.359149321.20310.6710021 Turkey8.37504838482.602163168812.033835.84757712.120316.115713 Belgium10.04505372412.02900145079.6267937.70475910.522487.875272 Cote d'Ivoire6.4789794347-1.230011069526.2433215.2553926.2630812.65963 Iran7.4205845762.423024302313.796233.89431514.321193.533688 India6.09427875883.585117687425.2364212.7480925.2629611.98164 Suriname7.6070245068-1.087064318826.6109322.1415326.6964921.5842 Senegal6.40465650280.055498592426.91849.22775527.444426.163564 Togo5.7111102088-0.376103780426.0722112.7969226.3278211.59565 Rwanda5.56366953251.252000312719.1293910.7603520.258510.81682 Peru7.57844619771.279218094313.261358.05694214.051188.142179 Mauritius8.27615943424.281374685324.0454516.5427323.9924214.99573 Philippines6.97803578120.530769127725.3897724.7269225.6405823.83464 Mali5.59794457730.967916696427.928138.58722628.475466.926775 Germany10.07052325791.84151121858.4291677.8535539.2613197.223675 Chile8.53206241924.485345734711.734666.04786411.396675.756211 Oman9.05966537041.877228525525.614251.24368325.767361.718448 Honduras6.98514412550.306134294423.7035815.8012224.7986912.35854 Argentina8.95002713222.272210933416.785628.50053717.187749.177779 Uganda5.56784436993.261433401822.2027411.8691922.3176211.9865 France10.01520911651.726098494110.323137.90013711.226397.400362 Lesotho6.07876168992.485163738912.330637.94103412.006616.443765 Laos5.7844003963.828208018523.551118.5960423.6084517.78357 Haiti6.6989262519-1.597174052523.8011815.1591224.6567110.86323 Paraguay7.41674236470.217254067722.610313.8609422.142516.53286 Costa Rica8.23559126712.25361749221.6997437.2471422.869739.86776 Mauritania6.517032455-0.871080318228.079413.88350728.87362.743884 United States10.50053423242.055704446513.062459.10727713.304728.762918 Botswana8.16687192812.465008424520.156295.03677921.533384.100937 Brazil8.22944531680.46974812721.3864913.7744422.3095613.2029 Trinidad and Tobago8.76953271144.960545686326.2712620.0292126.0596119.53647 Korea9.41385195666.467544334411.1875211.7375311.5642713.40523 Ireland10.17509224686.37678586249.00484910.224239.57795810.37711 Colombia7.83220507051.012220862221.2673820.5692321.2294719.03087 Indonesia6.65953773742.74789128325.5000524.9861925.7693918.55051 Belize8.12098498643.752138039325.4207223.6898425.8587423.32534 Algeria7.4759185462-0.164740644516.801555.64202517.135574.614381 Cuba7.9183005701-1.786452328925.3118513.2760725.3385410.69392 Uruguay8.83565002482.507645399616.9936811.0972517.3745511.01161 Cabo Verde7.13833007497.958015454423.365917.89818223.606068.581819 Norway10.54879495822.95879498343.295239.8865394.2822119.770261 Ecuador7.2760578860.011939686319.9049713.7328520.4068916.58907 Denmark10.33343541972.20161223157.64896.6445078.3912733.57791 Dominican Republic7.96175571343.152938660524.4703117.8468625.6609414.14335 Nicaragua6.91522871181.102240319724.7879216.9847226.5319514.15275 Sri Lanka6.76814431934.438963592726.60523.2658726.8792822.05306 China6.86627940798.655843282713.424239.93429313.9145510.40647 Japan10.55924540471.356308293412.9626417.954613.5576216.39544 Gambia, The6.387134578-0.034229887725.804713.7307326.408949.141487 Guinea-Bissau5.7305675933-0.437058263326.4686618.69826.8850514.93492 Hungary8.43907632041.89912986410.437766.08229210.612925.907116 Guatemala7.41715926771.460227830719.8414622.934221.8251218.68205 United Kingdom10.24529801582.05055025769.0410438.1306289.6193267.720697 Nepal5.43586070672.508794623220.4492415.2324320.3741714.7353 Jordan7.40951338230.48608551418.247152.72608817.661712.621536 Samoa7.34087883191.171336005126.0621230.90625.9522715.19527 Venezuela8.48509082060.422032290624.9950811.9416525.7487311.70615 Bolivia6.90533481191.7539490618.6229210.5647318.498419.714385 Iceland10.37405551311.64818716842.965888.3546142.4675398.012055 Greece9.39623501391.596620334214.957997.11712414.648265.710767 New Zealand9.52084277261.404064546511.9889412.2969411.9012610.24473 Cameroon6.4769598173-2.153587780224.2351620.2287224.3423417.56387 Italy9.90785758931.671002215911.8176410.3582112.3323711.61727 Burkina Faso5.54015520221.986588690627.419639.5970427.96378.097651 Guinea5.8957310951.245459328925.0310422.8603225.3737219.88388 Sweden10.29637093071.62046718725.2821566.2896886.0230345.778014 Morocco7.19664391151.514975137617.048154.35330217.194794.088074 Madagascar5.6822426745-1.187089028320.6784415.3514421.0581714.66957 Netherlands10.17158194832.74316720579.6668377.44704710.351877.761985 Gabon8.3249950203-0.533867964124.7728322.0746924.6961320.5727 Kenya5.9851513074-0.940087435620.3193311.052620.0608112.29716 Comoros6.4705188-0.405154735524.8030322.9824526.0219720.58245 Switzerland10.54187023780.7286960094.97507813.795245.80475611.75557 Congo, Dem. Rep.6.0044208826-8.474498001324.0728614.2309824.3359715.48968 Jamaica8.12701903681.126682665423.8138519.8462424.9929517.55477 Kuwait9.8222980241-2.867917947225.51010.93917725.977431.293124 Benin6.23715250671.702369936427.0211511.7530527.1250210.76115 Mexico8.87595997591.928195582218.617729.26213818.999398.251323 Niger5.2849352539-2.038653694927.787835.9691328.344294.570006 Malawi5.05232551061.968176058221.4263611.6372223.0839910.28716 Romania7.414517241-0.7718962138.934295.9747019.2096783.596791 Central African Republic5.5262768124-1.6089453724.7131414.9723824.4955914.09322 Tanzania6.01847721650.462086633622.361729.37331922.6339810.18079 Chad5.1105589763-1.433425934527.74918.17325428.090926.980453 Bhutan6.57673113754.34095201311.5509634.6053311.3761323.39394 Syrian Arab Republic7.07125860142.494926335917.283833.93127517.547453.316102 Luxembourg10.79417316323.62674950089.1651527.4082739.6022737.092182 Tunisia7.70157406723.286240995418.060594.86251718.985734.316459 Mozambique5.7663176932.790436047523.6686410.3452124.484179.191278 Poland8.41215574973.76989670677.6407285.8307668.3181396.146651 El Salvador7.60167116862.334291938822.5682318.0239924.2313214.27574 Burundi4.9160606296-3.022205968619.2873910.8761520.7532210.58038 Saudi Arabia9.12383760871.224633519824.689021.2221825.521490.8200489 Cambodia5.7123363311-0.932081399227.0639616.6937727.7829512.87399 1. Has the globe been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset. 2. Do hot countries tend to be poor (with lower per capita GDP)? Do dry countries tend to be poor? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present two relationships. Carefully interpret and explain. 3. 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. 4. 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. 5. 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.