Description: For 97 countries in the world, data are given for birth rates, death rates, infant death rates, life expectancies for males and females, and
Gross National Product.
Analyze these data to estimate the best model that describes the relationship between the response (Gross National Product) and the predictors (all other variables except for country). Can these variables be used to predict GNP? Which of these variables are the most important? Are there significant differences among the 6 groups of countries?
You can download the data from the BB in two formats: text file and excel. Stars indicate missing values. Do not forget to carry out regression diagnostic tests. Provide full justification for all the analysis steps and for how the final conclusion was reached upon.
Microsoft Word - STAT2118_Project_Instructions.docx 1 The following instructions are divided into four sets of steps: 1. Recode qualitative covariates and create dummy variables if needed 2. Conduct preliminary analyses a. Examine descriptive statistics of the continuous variables b. Check the linearity assumption 3. Conduct multiple linear regression analysis a. Run model with dependent and independent variables b. Model Check i. Examine collinearity diagnostics to check for multicollinearity ii. Examine residual plots to check error variance assumptions (i.e., normality and homogeneity of variance, etc.) iii. Examine influence diagnostics to check for outliers and leverage points iv. Examine significance of coefficient estimates to trim the model c. Revise the model and rerun the analyses based on the results of steps i-iv d. Write the final regression equation and interpret the coefficient estimates 4. Report your findings and make final conclusions. Please submit detailed report with SAS program and output attached. Microsoft Word - STAT2118_Project.docx Stat 2118 Data Analysis Project Due Tuesday, Dec. 12, 2017 Datafile Name: The Statistics of Poverty and Inequality Reference: Day, A. (ed.) (1992), The Annual Register 1992, 234, London: Longmans. U.N.E.S.C.O. 1990 Demographic Year Book (1990), New York: United Nations. Authorization: free use Description: For 97 countries in the world, data are given for birth rates, death rates, infant death rates, life expectancies for males and females, and Gross National Product. Analyze these data to estimate the best model that describes the relationship between the response (Gross National Product) and the predictors (all other variables except for country). Can these variables be used to predict GNP? Which of these variables are the most important? Are there significant differences among the 6 groups of countries? You can download the data from the BB in two formats: text file and excel. Stars indicate missing values. Do not forget to carry out regression diagnostic tests. Provide full justification for all the analysis steps and for how the final conclusion was reached upon. Number of cases: 97 Variable Description: 8 variables Columns 1 Live birth rate per 1,000 of population 2 Death rate per 1,000 of population 3 Infant deaths per 1,000 of population under 1 year old 4 Life expectancy at birth for males 5 Life expectancy at birth for females 6 Gross National Product per capita in U.S. dollars 7 Country Group 1 = Eastern Europe 2 = South America and Mexico 3 = Western Europe, North America, Japan, Australia, New Zealand 4 = Middle East 5 = Asia 6 = Africa 8 Country Values are aligned and delimited by blanks. Missing values are denoted with *. The Data: 24.7 5.7 30.8 69.6 75.5 600 1 Albania 12.5 11.9 14.4 68.3 74.7 2250 1 Bulgaria 13.4 11.7 11.3 71.8 77.7 2980 1 Czechoslovakia 12 12.4 7.6 69.8 75.9 * 1 Former_E._Germany 11.6 13.4 14.8 65.4 73.8 2780 1 Hungary 14.3 10.2 16 67.2 75.7 1690 1 Poland 13.6 10.7 26.9 66.5 72.4 1640 1 Romania 14 9 20.2 68.6 74.5 * 1 Yugoslavia 17.7 10 23 64.6 74 2242 1 USSR 15.2 9.5 13.1 66.4 75.9 1880 1 Byelorussian_SSR 13.4 11.6 13 66.4 74.8 1320 1 Ukrainian_SSR 20.7 8.4 25.7 65.5 72.7 2370 2 Argentina 46.6 18 111 51 55.4 630 2 Bolivia 28.6 7.9 63 62.3 67.6 2680 2 Brazil 23.4 5.8 17.1 68.1 75.1 1940 2 Chile 27.4 6.1 40 63.4 69.2 1260 2 Columbia 32.9 7.4 63 63.4 67.6 980 2 Ecuador 28.3 7.3 56 60.4 66.1 330 2 Guyana 34.8 6.6 42 64.4 68.5 1110 2 Paraguay 32.9 8.3 109.9 56.8 66.5 1160 2 Peru 18 9.6 21.9 68.4 74.9 2560 2 Uruguay 27.5 4.4 23.3 66.7 72.8 2560 2 Venezuela 29 23.2 43 62.1 66 2490 2 Mexico 12 10.6 7.9 70 76.8 15540 3 Belgium 13.2 10.1 5.8 70.7 78.7 26040 3 Finland 12.4 11.9 7.5 71.8 77.7 22080 3 Denmark 13.6 9.4 7.4 72.3 80.5 19490 3 France 11.4 11.2 7.4 71.8 78.4 22320 3 Germany 10.1 9.2 11 65.4 74 5990 3 Greece 15.1 9.1 7.5 71 76.7 9550 3 Ireland 9.7 9.1 8.8 72 78.6 16830 3 Italy 13.2 8.6 7.1 73.3 79.9 17320 3 Netherlands 14.3 10.7 7.8 67.2 75.7 23120 3 Norway 11.9 9.5 13.1 66.5 72.4 7600 3 Portugal 10.7 8.2 8.1 72.5 78.6 11020 3 Spain 14.5