1 Math 133 Project #1: Descriptive Statistics You will use GapMinder.org to access data for the purpose of exploring descriptive statistics and graphs with Microsoft Excel. The entire assignment is to be done in multiple worksheets in ONE Excel workbook file and submitted in MyMathLab under \"Course Tools/Document Sharing\" by the due date. You should NEVER touch your calculator throughout the entire project. ALL calculations and tables must be formed entirely using Excel or NO credit will be given. Remember to save your file every step of the way. For both tasks, use the random year assigned by your instructor. (Video tutorials available at: http://bit.ly/133proj1) Task #1: GDP Data 61 points Part 1: Download the GDP Data = 1 points 1. Go to the website h ttp://www.gapminder.org 2. Click on \"Data\" 3. In the search bar, type \"GDP/capita\" 4. Locate and click on the Excel Download for \"Income per person (GDP/capita, PPP$ inflationadjusted)\". 5. Save the downloaded file as yourlastname_project1 to your computer. Part 2: Cleaning up the GDP Spreadsheet = 4 points You are going to only consider the data for the year you were assigned by your instructor. 1. Rename the \"Data\" tab \"GDP.\" 2. Rename the \"About\" tab as \"About GDP\". Leave the other tabs in the Project 1 Workbook. 3. Delete all columns for years other than the one you were assigned. 4. Sort the data in ascending order (be sure to \"expand the selection\" to include the country names). Part 3: GDP Statistics = 18 points Compute the following in the \"GDP\" tab Using Excel's builtin formulas and cellreferences: find and label the following in a clear manner. Note: You MUST use EXCEL to find these values. 1. Mean 2. Median 3. Count (i.e. the number of countries reporting data in your year) 4. Variance (consider the data a population) 5. Standard Deviation (consider the data a population) 6. Minimum What country (or countries) has this value? 7. Maximum What country (or countries) has this value? 8. Range 9. Class Width for a frequency distribution with 20 classes (Hint: Remember to round up to a convenient number) 2 Part 4: GDP Distributions = 10 points Construct the following in the \"GDP\" tab Construct a distribution of the data with Excel including each of the following in their own column. Be sure the table is clear, welllabeled, and organized. Remember distribution creation was covered in sections 2.2 and 2.3. Note: Use Excel formulas and functions to find all values of the table. 1. Class Limits/Boundaries using the class width found in Part 3 above and start the first class' lower class limit at 0 (Note: You may need more than 20 classes in order to accommodate the maximum value, this is fine) 2. Class Midpoints 3. Class Frequencies 4. Class Relative Frequencies (Use Excel to find, with cell references) Part 5: GDP Graphs = 12 points Construct the following in the \"GDP\" tab Construct each of the following graphs with Excel using the distribution made in Part 4 above. Be sure that they are welllabeled and organized in such a way that they stacked on top of one another. Remember graph creation was covered in sections 2.2 and 2.3. 1. Frequency Histogram 2. Relative Frequency Histogram 3. Frequency Polygon 4. Relative Frequency Polygon Part 6: GDP Analysis = 16 points Answer the following in the \"GDP\" tab Answer the following with complete and clearly numbered entries. 1. Describe the variable \"Country\" by type (Qualitative, Quantitative Discrete, or Quantitative Continuous) and level of measurement. (Nominal, Ordinal, Interval, Ratio) 2. Describe the variable is \"GDP/Capita\" by type (Qualitative, Quantitative Discrete, or Quantitative Continuous) and level of measurement. (Nominal, Ordinal, Interval, Ratio) 3. Judging from the mean and median, what seems to be the shape of the distribution? Explain. 4. Suppose that you need to discuss the \"average\" and \"spread\" of GDP for your assigned year to the news media. What are the more appropriate measures to use for each? Explain. 5. What class appears to be the modal class for assigned year? 6. What is the z score for the U.S. GDP/Capita in your assigned year? (calculate with Excel) 7. Was the U.S. above or below the mean? Explain using the zscore. 8. Was the U.S. unusually high or low? Explain using the zscore. Remember to save your file every step of the way! 3 Task #2: Mortality 39 points Part 1: Download the Mortality and Life Expectancy Data = 1 points 1. Go to the website www.gapminder.org, click on \"Data\" and in the search bar, type \"Infant Mortality\" 2. Locate and click on the Excel Download for \"Infant mortality (rate per 1,000 births)\". 3. Again in the \"Data\" section of www.gapminder.org, search for \"Life Expectancy\" 4. Locate and click on the Excel Download for \"Life expectancy at birth, with projections\" Note: you should now have TWO Excel spreadsheets downloaded... we will merge them into the Project 1 Workbook in part 2. Part 2: Cleaning up the Mortality Spreadsheet = 4 points 1. Copy \"About\" tab from Infant Mortality spreadsheet into your Project 1 Workbook and rename it \"About Mortality\" 2. Copy \"About\" tab from Life Expectancy spreadsheet into your Project 1 Workbook and rename it \"About Life Expectancy\" 3. Insert a new tab into your Project 1 Workbook and name it \"Mortality\". 4. You are going to only consider the data for the year you were assigned by your instructor (same year as with Task #1 - GDP) so delete all columns for years other than the one you were assigned in BOTH the Infant Mortality and Life Expectancy Spreadsheets. 5. Copy and paste the data from the Life Expectancy spreadsheet and the Infant Mortality spreadsheet into the \"Mortality\" worksheet/tab in your Project 1 workbook. 6. Rename the columns with appropriate labels. Be sure to indicate clearly which year you are using. 7. Make sure that all the countries align with their respective data points, deleting any rows where the country is missing either of the variables. (Hint: SORT will help with this) Be careful as some countries names are slightly different in the two tabs (e.g., Yemen and Yemen Republic, Saint Lucia and St. Lucia, etc.) Part 3: Mortality Scatterplot = 7 points Construct the following in the \"Mortality\" tab Using Infant Mortality as the explanatory variable, construct a scatter diagram of the data using Excel in the \"Mortality\" tab. o Be sure to appropriately label the graph and its axes o Include the linear regression line on the graph o Include the equation of the regression line and the R2 value on the graph Remember to save your file every step of the way! 4 Part 4: Mortality Analysis = 27 points Answer the following in the \"Mortality\" tab Answer the following with complete and clearly numbered entries. (Hint: the \"About Infant Mortality\" and \"About Life Expectancy\" tabs have information on what the numbers represent and their respective units) 1. Describe the variable \"Life Expectancy\" by type (Qualitative, Quantitative Discrete, or Quantitative Continuous) and level of measurement. (Nominal, Ordinal, Interval, Ratio) 2. Describe the variable \"Infant Mortality\" by type (Qualitative, Quantitative Discrete, or Quantitative Continuous) and level of measurement. (Nominal, Ordinal, Interval, Ratio) 3. State the linear regression equation (Give all decimal places) 4. Interpret the regression equation's slope in the context of the situation. 5. Interpret the regression equation's yintercept in the context of the situation. 6. State the value of the coefficient of determination. 7. Compute the value of the correlation coefficient with Excel (Hint: SQRT is the Excel function for taking a square root) 8. How strong is the relationship between the two variables (weak, moderate, strong)? 9. What type of relationship exists between the two variables (Positive linear, negative linear)? 10. Why does the relationship between infant mortality and life expectancy make sense and what does it reveal about countries in your assigned year? 11. What was the U.S.'s infant mortality in your assigned year? 12. What was the U.S.'s actual life expectancy in your assigned year? 13. Compute the predicted U.S. life expectancy for your assigned year using the linear regression model. (Give 3 decimal places) 14. Compute the U.S.'s residual. (Give 3 decimal places) 15. Explain what the residual reveals about how well healthcare was doing in the U.S. in your assigned year. Lastly, save your file one last time and then upload the file under \"Course Tools\GDP per capita Congo, Dem. Rep. Malawi Somalia Burundi Ethiopia Mozambique Niger Central African Republic Rwanda Afghanistan Sierra Leone Timor-Leste Guinea Togo Burkina Faso Eritrea Guinea-Bissau Uganda Madagascar North Korea Comoros Gambia Mali Haiti Benin Zimbabwe Solomon Islands Kiribati Nepal Chad Tajikistan Papua New Guinea Liberia Lesotho Tanzania Bangladesh Senegal Cambodia Djibouti Kenya Kyrgyz Republic Sao Tome and Principe Cameroon Myanmar Ghana Zambia Vanuatu Cote d'Ivoire Sudan Tuvalu Lao Uzbekistan 2006 First class Lower limit 0Lower Class Limit Upper class limit 569 Class Width 756 604 0 755.9 615 756 1511.9 704 1512 2267.9 796 2268 3023.9 801 3024 3779.9 804 3780 4535.9 869 4536 5291.9 1038 5292 6047.9 1173 6048 6803.9 1185 6804 7559.9 1206 7560 8315.9 1212 8316 9071.9 1228 9072 9827.9 1271 9828 10583.9 1272 10584 11339.9 1300 11340 12095.9 1304 12096 12851.9 1421 12852 13607.9 1461 13608 14363.9 1499 14364 15119.9 1521 15120 15875.9 1560 15876 16631.9 1576 16632 17387.9 1592 17388 18143.9 1629 18144 18899.9 1672 18900 19655.9 1683 19656 20411.9 1741 20412 21167.9 1755 21168 21923.9 1800 21924 22679.9 1819 22680 23435.9 1873 23436 24191.9 1873 24192 24947.9 1880 24948 25703.9 2038 25704 26459.9 2099 26460 27215.9 2136 27216 27971.9 2315 27972 28727.9 2319 28728 29483.9 2418 29484 30239.9 2546 30240 30995.9 2547 30996 31751.9 2593 31752 32507.9 2652 32508 33263.9 2752 33264 34019.9 2761 34020 34775.9 2827 34776 35531.9 2858 35532 36287.9 3048 36288 37043.9 3126 37044 37799.9 3211 37800 38555.9 Micronesia, Fed. Sts. Marshall Islands Mauritania South Sudan Moldova India Vietnam Nicaragua Pakistan Honduras West Bank and Gaza Yemen Nigeria Bolivia Bhutan Guyana Nauru Tonga Cape Verde Philippines Georgia Congo, Rep. Samoa Angola Morocco Swaziland Syria Armenia Paraguay Sri Lanka China Mongolia Guatemala Kosovo Turkmenistan El Salvador Indonesia Fiji Albania Namibia Ukraine Peru Bosnia and Herzegovina Belize Ecuador Jamaica Egypt Tunisia Dominica Dominican Republic Macedonia, FYR Colombia Jordan 3312 3333 3413 3455 3487 3514 3687 3846 4076 4128 4193 4320 4353 4716 4767 4837 4897 4920 4937 4967 5116 5138 5406 5445 5725 5733 5888 6020 6180 6223 6360 6480 6658 6982 7137 7148 7168 7253 7476 7696 7848 7975 8242 8360 8802 8954 8990 9109 9172 9568 9704 9759 9818 38556 39312 40068 40824 41580 42336 43092 43848 44604 45360 46116 46872 47628 48384 49140 49896 50652 51408 52164 52920 53676 54432 55188 55944 56700 57456 58212 58968 59724 60480 61236 61992 62748 63504 64260 65016 65772 66528 67284 68040 68796 69552 70308 71064 71820 72576 73332 74088 74844 75600 76356 77112 77868 39311.9 40067.9 40823.9 41579.9 42335.9 43091.9 43847.9 44603.9 45359.9 46115.9 46871.9 47627.9 48383.9 49139.9 49895.9 50651.9 51407.9 52163.9 52919.9 53675.9 54431.9 55187.9 55943.9 56699.9 57455.9 58211.9 58967.9 59723.9 60479.9 61235.9 61991.9 62747.9 63503.9 64259.9 65015.9 65771.9 66527.9 67283.9 68039.9 68795.9 69551.9 70307.9 71063.9 71819.9 72575.9 73331.9 74087.9 74843.9 75599.9 76355.9 77111.9 77867.9 78623.9 St. Vincent and the Grenadines Maldives Azerbaijan St. Lucia Serbia Iraq Costa Rica Grenada Thailand South Africa Panama Belarus Montenegro Algeria Lebanon Botswana Suriname Brazil Argentina Mauritius Bulgaria Uruguay Palau Iran Romania Mexico Cuba Barbados Turkey Gabon Venezuela Kazakhstan Chile Poland Malaysia Seychelles Latvia Russia Lithuania Croatia Turks and Caicos Islands Slovak Republic St. Kitts and Nevis Hungary Greenland Antigua and Barbuda Estonia Bahamas Malta South Korea Israel Portugal Czech Republic 10038 10157 10711 10858 11130 11271 11371 11380 11400 11597 11886 12010 12025 12088 12171 12241 12553 12732 12913 13188 13228 13489 14298 15127 15246 15516 15901 15913 16013 16059 16178 17109 17554 17959 18582 19086 19367 19660 19923 20358 20690 21162 21997 22938 23031 24016 24047 25530 25983 26734 26908 27111 27256 78624 79380 80136 80892 81648 82404 83160 83916 84672 85428 86184 86940 87696 88452 89208 89964 90720 91476 92232 92988 93744 94500 95256 96012 96768 97524 98280 99036 99792 100548 101304 102060 102816 103572 104328 105084 105840 106596 107352 108108 108864 109620 110376 111132 111888 112644 113400 114156 114912 115668 116424 117180 117936 79379.9 80135.9 80891.9 81647.9 82403.9 83159.9 83915.9 84671.9 85427.9 86183.9 86939.9 87695.9 88451.9 89207.9 89963.9 90719.9 91475.9 92231.9 92987.9 93743.9 94499.9 95255.9 96011.9 96767.9 97523.9 98279.9 99035.9 99791.9 100547.9 101303.9 102059.9 102815.9 103571.9 104327.9 105083.9 105839.9 106595.9 107351.9 108107.9 108863.9 109619.9 110375.9 111131.9 111887.9 112643.9 113399.9 114155.9 114911.9 115667.9 116423.9 117179.9 117935.9 118691.9 Slovenia Libya Trinidad and Tobago Greece New Zealand Equatorial Guinea Spain Japan Cyprus France Puerto Rico United Kingdom Taiwan Italy Germany Australia Iceland Saudi Arabia Finland Aruba Belgium Canada Austria Andorra Sweden Oman Bahrain Hong Kong, China Netherlands Denmark Ireland United States Switzerland Monaco San Marino Cayman Islands Bermuda Norway Singapore Macao, China Brunei Luxembourg Kuwait United Arab Emirates Qatar 28096 28256 28790 31399 32281 33468 34206 34468 34528 37001 37327 37504 37661 37761 39352 39416 39818 39958 40115 40209 40661 41012 42036 42738 42873 42933 43358 43521 44823 45437 49048 50599 52786 54636 55241 56971 58785 64573 65331 71760 76287 91810 94642 95637 127563 118692 119448 120204 120960 121716 122472 123228 123984 124740 125496 126252 127008 127764 128520 129276 130032 130788 131544 132300 133056 133812 134568 135324 136080 136836 137592 138348 139104 139860 140616 141372 142128 142884 143640 144396 145152 145908 146664 147420 148176 148932 149688 150444 151200 151956 119447.9 120203.9 120959.9 121715.9 122471.9 123227.9 123983.9 124739.9 125495.9 126251.9 127007.9 127763.9 128519.9 129275.9 130031.9 130787.9 131543.9 132299.9 133055.9 133811.9 134567.9 135323.9 136079.9 136835.9 137591.9 138347.9 139103.9 139859.9 140615.9 141371.9 142127.9 142883.9 143639.9 144395.9 145151.9 145907.9 146663.9 147419.9 148175.9 148931.9 149687.9 150443.9 151199.9 151955.9 152711.9 Mean Median Count 17464.85 31840 203 Variance Standard Deveiation Minimum Maximum Range Class Width (20) 4.2E+008 20434.8 Country: Congo, DEM, 569 REP 127563 Country:Qatar 15121.8 756.0898 Rounded- 756 GDP Class limit 0-755.9 756-1511.9 1512-2267.9 2268-3023.9 3024-3779.9 3780-4535.9 4536-5291.9 5292-6047.9 6048-6803.9 6804-7559.9 7560-8315.9 8316-9071.9 9072-9827.9 9828-10583.9 10584-11339.9 11340-12095.9 12096-12851.9 12852-13607.9 13608-14363.9 14364-15119.9 15120-15875.9 15876-16631.9 16632-17387.9 17388-18143.9 18144-18899.9 18900-19655.9 19656-20411.9 20412-21167.9 21168-21923.9 21924-22679.9 22680-23435.9 23436-24191.9 24192-24947.9 24948-25703.9 25704-26459.9 26460-27215.9 27216-27971.9 27972-28727.9 28728-29483.9 29484-30239.9 30240-30995.9 30996-31751.9 31752-32507.9 32508-33263.9 33264-34019.9 34020-34775.9 34776-35531.9 35532-36287.9 36288-37043.9 37044-37799.9 37800-38555.9 Class midpoint Frequency Relative Frequency 0 0 378 3 0.0147783251 1134 17 0.0837438424 1890 17 0.0837438424 2646 11 0.0541871921 3402 11 0.0541871921 4158 6 0.0295566502 4914 9 0.0443349754 5670 7 0.0344827586 6426 6 0.0295566502 7182 6 0.0295566502 7938 4 0.0197044335 8694 4 0.0197044335 9450 6 0.0295566502 10206 2 0.0098522167 10962 4 0.0197044335 11718 8 0.039408867 12474 4 0.0197044335 13230 4 0.0197044335 13986 1 0.0049261084 14742 0 0 15498 4 0.0197044335 16254 6 0.0295566502 17010 1 0.0049261084 17766 1 0.0049261084 18522 1 0.0049261084 19278 2 0.0098522167 20034 4 0.0197044335 20790 2 0.0098522167 21546 0 0 22302 1 0.0049261084 23058 2 0.0098522167 23814 2 0.0098522167 24570 0 0 25326 1 0.0049261084 26082 1 0.0049261084 26838 3 0.0147783251 27594 1 0.0049261084 28350 2 0.0098522167 29106 1 0.0049261084 29862 0 0 30618 0 0 31374 1 0.0049261084 32130 1 0.0049261084 32886 0 0 33642 3 0.0147783251 34398 0 0 35154 0 0 35910 0 0 36666 1 0.0049261084 37422 3 0.0147783251 38178 0 0 18 16 14 12 10 Frequency 8 6 4 2 0 0.09 0.08 0.07 0.06 0.05 0.04 Relative Frequency 0.03 0.02 0.01 0 0.02 0.01 38556-39311.9 39312-40067.9 40068-40823.9 40824-41579.9 41580-42335.9 42336-43091.9 43092-43847.9 43848-44603.9 44604-45359.9 45360-46115.9 46116-46871.9 46872-47627.9 47628-48383.9 48384-49139.9 49140-49895.9 49896-50651.9 50652-51407.9 51408-52163.9 52164-52919.9 52920-53675.9 53676-54431.9 54432-55187.9 55188-55943.9 55944-56699.9 56700-57455.9 57456-58211.9 58212-58967.9 58968-59723.9 59724-60479.9 60480-61235.9 61236-61991.9 61992-62747.9 62748-63503.9 63504-64259.9 64260-65015.9 65016-65771.9 65772-66527.9 66528-67283.9 67284-68039.9 68040-68795.9 68796-69551.9 69552-70307.9 70308-71063.9 71064-71819.9 71820-72575.9 72576-73331.9 73332-74087.9 74088-74843.9 74844-75599.9 75600-76355.9 76356-77111.9 77112-77867.9 77868-78623.9 38934 39690 40446 41202 41958 42714 43470 44226 44982 45738 46494 47250 48006 48762 49518 50274 51030 51786 52542 53298 54054 54810 55566 56322 57078 57834 58590 59346 60102 60858 61614 62370 63126 63882 64638 65394 66150 66906 67662 68418 69174 69930 70686 71442 72198 72954 73710 74466 75222 75978 76734 77490 78246 0 7 0 1 1 3 2 0 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0.0344827586 0 0.0049261084 0.0049261084 0.0147783251 0.0098522167 0 0.0049261084 0.0049261084 0 0 0 0.0049261084 0 0.0049261084 0 0 0.0049261084 0 0 0.0049261084 0 0 0.0049261084 0 0.0049261084 0 0 0 0 0 0 0 0.0049261084 0.0049261084 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0049261084 0 0 0 0 18 16 14 12 10 Frequency 8 6 4 2 0 Relative Frequency 78624-79379.9 79380-80135.9 80136-80891.9 80892-81647.9 81648-82403.9 82404-83159.9 83160-83915.9 83916-84671.9 84672-85427.9 85428-86183.9 86184-86939.9 86940-87695.9 87696-88451.9 88452-89207.9 89208-89963.9 89964-90719.9 90720-91475.9 91476-92231.9 92232-92987.9 92988-93743.9 93744-94499.9 94500-95255.9 95256-96011.9 96012-96767.9 96768-97523.9 97524-98279.9 98280-99035.9 99036-99791.9 99792-100547.9 100548-101303.9 101304-102059.9 102060-102815.9 102816-103571.9 103572-104327.9 104328-105083.9 105084-105839.9 105840-106595.9 106596-107351.9 107352-108107.9 108108-108863.9 108864-109619.9 109620-110375.9 110376-111131.9 111132-111887.9 111888-112643.9 112644-113399.9 113400-114155.9 114156-114911.9 114912-115667.9 115668-116423.9 116424-117179.9 117180-117935.9 117936-118691.9 79002 79758 80514 81270 82026 82782 83538 84294 85050 85806 86562 87318 88074 88830 89586 90342 91098 91854 92610 93366 94122 94878 95634 96390 97146 97902 98658 99414 100170 100926 101682 102438 103194 103950 104706 105462 106218 106974 107730 108486 109242 109998 110754 111510 112266 113022 113778 114534 115290 116046 116802 117558 118314 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0049261084 0 0 0 0.0049261084 0.0049261084 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 118692-119447.9 119448-120203.9 120204-120959.9 120960-121715.9 121716-122471.9 122472-123227.9 123228-123983.9 123984-124739.9 124740-125495.9 125496-126251.9 126252-127007.9 127008-127763.9 127764-128519.9 128520-129275.9 129276-130031.9 130032-130787.9 130788-131543.9 131544-132299.9 132300-133055.9 133056-133811.9 133812-134567.9 134568-135323.9 135324-136079.9 136080-136835.9 136836-137591.9 137592-138347.9 138348-139103.9 139104-139859.9 139860-140615.9 140616-141371.9 141372-142127.9 142128-142883.9 142884-143639.9 143640-144395.9 144396-145151.9 145152-145907.9 145908-146663.9 146664-147419.9 147420-148175.9 148176-148931.9 148932-149687.9 149688-150443.9 150444-151199.9 151200-151955.9 151956-152711.9 119070 119826 120582 121338 122094 122850 123606 124362 125118 125874 126630 127386 128142 128898 129654 130410 131166 131922 132678 133434 134190 134946 135702 136458 137214 137970 138726 139482 140238 140994 141750 142506 143262 144018 144774 145530 146286 147042 147798 148554 149310 150066 150822 151578 152334 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 203 0 0 0 0 0 0 0 0 0 0.0049261084 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PART 5 GDP GRAPHS Frequency Histogram 18 16 14 12 10 Frequency 8 6 4 2 0 GDP Relative Frequency Histogram 0.09 0.08 0.07 0.06 0.05 0.04 Relative Frequency 0.03 0.02 0.01 0 GDP per capita 0.02 0.01 0 GDP per capita Frequency Polygon 18 16 14 12 10 Frequency 8 6 4 2 0 GRP per capita midpoint Relative frequency polygon 0.09 0.08 0.07 0.06 0.05 Relative Frequency 0.04 0.03 0.02 0.01 0 GDP per capita midpoints 0 GDP per capita midpoints 36327 Income per person (fixed PPP$) (version 17) Gapminder has compiled the data you see in this graph from several sources, such as official international statistics, va sources and our own estimates. The link below takes you to Gapminder's documentation page, which contains the details on how the compilation was d sources for each observation. Definition and explanations Definition of indicator Main sources: Who has compiled the data and how was it done Compiled by: Link to the full documentation of this indicator Version: Present version uploaded: Internal note: Updates Oct-15 2015 mars Nov 24 2014 4/21/2014 2013--10--23 6/27/2013 2012 -- 09 - 07 2012-05-23 2011-06-18 2010/07/12 2010/04/26 2009/12/18 2009/10/01 2009/04/27 2008/12/22 2008/12/10 2008/12/04 son (fixed PPP$) (version 17) a you see in this graph from several sources, such as official international statistics, various historical minder's documentation page, which contains the details on how the compilation was done and the Gross Domestic Product per capita by Purchasing Power Parities (in international dollars, fixed 2011 prices). The inflation and differences in the cost of living between countries has been taken into account. Cross-country data for 2011 is mainly based on the 2011 round of the International Comparison Program. Estimates based on other sources were used for the other countries. Real growth rates were linked to the 2011 levels. Several sources are used for these growth rates, such as the data of Angus Maddison. Follow the link below to download the detailed documentation. d how was it done Mattias Lindgren, Gapminder www.gapminder.org/downloads/documentation/gd001 15 2015 mars data taken from file u935.104 Version 17. preliminary. Version 15. preliminary. Updated to the latest PPP 2011 from World Bank Version 14: updated UK, cambodia, lebanon, china, malta, greenland. Added interpolations for all years. Version 13: We revised the historical guesstimates for the Baltic states. Version 12: (a) new updates from IMF and a few other complementary sources; (b) revised historical series of former USSR republics so we now have yearly data rather than only benchmark years; ( c ) new data for Kosovo back to 1990; (d) new swedish data for 1560 to 2005 incorporated. Much of what is written about Sweden in the pdf documentation from 2011 in section 5.3 is now irrelevant (since that refers to the previous series which were much lower in 1800); ( e ) deleted the data before 1800 for Finland, Denmark & Norway since they seemed to unreiable inlight of the new swedish data. Version 11: added South Sudan. Version 10:series we added did some revisions for 2006-2010. IMF World Extending up to2011 2010,and by using real backward GDP per capita growth from IMF World Economic Outlook (2012, april) was the main source for these updates. Economic Outlook. The following are not in the WEO data, and were estimated based on CIA World Factbook growth figures: Korea Dem Rep, Cuba, Kosovo, Macao, Taiwan, Bermuda, Macedonia, Puerto Rico, Somalia, Timor Lests, Trinidad and Tobago, West Bank & Gaza. Kosovos series was extended five years backwards from 2005. /Ola Version 9: revision of a large number of countries. The revisions has mainly been made for the period before 1950. Version 8: data from IMF for 2009 added + some revisions since 2005 + added a few missing observations that were mistakingly deleted in previous update Version 7: data for 2008 added Version 6: Congo Dem. Rep. revised Version 5: updated Swedish data, revised Myanmar, raised lowest level, added data from Barro & Urs??a Complete revision of the whole data set. This new version is based on the PPPs from the 2005 round from ICP. Previous version of the data set is available as a separate indicator. Data for many other countries added upto 2007 Data for Iraq & Zimbabwe added upto 2007 Infant mortality rate 2006 Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Cook Is Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Infant mortality 2006 Life expectancy 2006 82.30 17.40 27.60 119.40 9.80 14.60 19.80 4.70 4.00 41.50 12.50 9.00 48.40 14.90 6.80 4.00 18.00 76.90 42.80 44.40 7.20 43.80 18.20 7.30 12.90 82.70 76.20 49.80 75.30 5.20 23.40 108.10 99.40 7.60 18.70 17.80 69.20 93.90 57.40 10.50 8.90 86.70 5.60 4.90 3.70 4.20 3.90 69.40 14.40 29.60 23.70 28.10 20.00 89.00 45.40 57.582 76.124 70.02 49.041 74.729 75.036 73.73 81.63 80.1 69.466 74.011 75.595 67.86 74.194 69.51 79.55 72.264 57.549 64.741 65.075 75.337 46.565 72.022 77.178 72.64 53.227 50.526 68.143 52.173 80.78 72.548 45.547 48.053 78.337 74.253 72.545 59.309 48.043 54.658 78.577 47.705 75.702 77.942 78.771 76.78 78.32 58.38 71.84 74.794 69.645 70.965 49.28 59.222 Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati North Korea South Korea Kuwait Kyrgyz Republic Lao Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico 5.30 65.30 20.20 2.90 3.70 49.00 55.60 20.20 3.80 55.60 4.60 12.30 32.30 81.90 86.70 34.10 64.10 24.1 6.8 2.30 53.90 32.10 20.50 32.40 4.10 4.3 3.70 16.70 2.70 19.9 26.80 51.80 49.50 25.80 4.50 10 33.00 67.80 10.10 11.20 87.50 81.90 18.60 7.70 2.60 11.6 50.60 67.10 6.90 17.2 93.20 5.90 32.40 74.00 14 16.20 73.13 57.63 68.567 79.49 80.88 60.477 57.021 73.29 79.7185338183 59.203 79.587 71.687 69.955 53.362 52.626 64.758 59.841 71.702 73.45 81.12 64.412 69.171 71.797 69.038 79.73 80.37 81.59 71.775 82.68 72.753 65.439 55.635 66.647 68.574 79.245 73.642 66.686 65.057 70.53 77.506 44.22 56.164 73.779 71.47 79.44 74.206 61.649 49.94 73.811 74.593 52.006 78.514 60.434 72.628 75.871 Micronesia, Fed. Sts. Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland 36.6 16.00 3.60 32.70 8.80 8.20 33.20 86.60 51.70 44.10 31.70 44.10 4.30 5.3 25.30 78.7 93.40 23.30 3.1 10.6 78.70 18.80 19.00 54.70 23.10 20.10 26.90 6.30 3.5 8.60 16.70 13.3 63.10 11.7 14.8 19.20 16.40 45.60 16.40 54.00 7.30 12.2 124.5 2.30 8.20 3.30 27.60 104.90 50.00 4.60 11.4 58.50 25.00 76.80 2.8 4.20 68.11 68.078 65.587 73.819 69.352 48.064 63.796 57.394 65.252 80 79.98 72.355 54.726 49.236 80.55 74.495 65.373 76.146 60.975 71.509 72.724 67.598 75.35 78.94 77.435 72.654 66.65 57.026 73.401 71.467 71.127 64.963 73.968 61.245 73.014 72.328 42.893 80.626 74.46 78.24 65.899 52.708 51.739 80.94 73.623 60.317 69.102 46.276 80.95 81.75 Syria Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela West Bank and Gaza Vietnam Yemen Zambia Zimbabwe 15.40 51.40 55.2 14.60 63.90 65.70 14.40 23.30 18.5 21.70 56.20 63.40 12.00 8.20 5.00 6.70 12.00 44.60 23.50 15.4 21.70 22 51.9 64.90 60.3 74.783 65.809 54.929 72.725 63.687 54.385 71.635 69.078 74.323 72.858 64.522 53.809 67.96 75.549 79.53 77.7 76.079 67.455 69.533 73.436 71.922 74.929 61.728 48.613 45.157 Life expectancy vs Infant mortality 90 80 70 60 f(x) = - 0.3177783576x + 78.5622963435 R?? = 0.8577411959 50 Life expectancy 40 30 20 10 0 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 Infant mortality rate Infant mortality rate (per 1,000 live birth) Gapminder has compiled the data you see in this graph from several sources, such as official international statistics, various historical sources and our own estimates. The link below takes you to Gapminder's documentation page, which contains the details on how the compilation was done and the sources for each observation. Definition and explanations Definition of indicator (by Unicef) Who has compiled the data and how was it done Link to the documentation of how this indicator was compiled, for datapoints that are not from CME. Version Uploaded Compiled by Authors Souces used: CME info estimates as of October 2015 Unicef estimates (the original link to the dataset not longer available) Mitchell, BR (1998a) International Historical Statistics. Africa, Asia & Oceania 1750-1993. Third edition. Basingstoke: Macmillan. Mitchell, BR (1998b) International Historical Statistics. The Americas 1750-1993. Fourth edition. Basingstoke: Macmillan. Mitchell, BR (1998c) International Historical Statistics. Europe 1750-1993. Fourth edition. Basingstoke: Macmillan. Human Mortality Database r 1,000 live birth) see in this graph from several sources, such as official al sources and our own estimates. r's documentation page, which contains the details on how rces for each observation. The probability that a child born in a specific year will die before reaching the age of one, if subject to current age-specific mortality rates. Expressed as a rate per 1,000 live births. http://www.gapminder.org/downloads/documentation/gd002 v4 October 18 2015 www.gapminder.org Klara Johannson, Mattias Lindgren, Ola Rosling www.childmortality.org www.childinfo.org Not available online Not available online Not available online www.mortality.org Life expectancy at birth (years) with projection Gapminder has compiled the data you see in this graph from several sources, such as official international statistics, v our own estimates. The link below takes you to Gapminder's documentation page, which contains the details on how the compilation was observation. Definition and explanations Indicator name Definition of indicator Unit of measurement Link to full documentation of this indicator: Version: Uploaded Data sources This indicator consists of two parts: a) a collection of estimates of average life expectancy from different sources. The main sources used: 1. Human Mortality Database, www.mortality.org 2. World Population Prospects: The 2010 Revision / United Nations Population Division, with projections 3. Publications and files by history prof. James C Riley 4. Human Lifetable Database, www.lifetable.de 5. Miscellaneous sources, see full documentation (link below) b) where no estimates are available before 1950, a constructed, simple model for showing levels and changes in histo WE DISCOURAGE THE USE OF THIS DATASET FOR STATISTICAL ANALYSIS PLEASE CONSULT THE FULL DOCUMENTATION FOR MORE DETAILS Specific information about this indicator Uploader Revised 2014 -- 11 -- 20 2014 -- 05 -- 07 2013 -- 07 -- 23 2011 -- 04 -- 11 birth (years) with projections his graph from several sources, such as official international statistics, various historical sources and mentation page, which contains the details on how the compilation was done and the sources for each Life expectancy at birth (years) with projections The average number of years a newborn child would live if current mortality patterns were to stay the same. Observations after 2010 are based on projections by the UN. Years http://www.gapminder.org/gapminder-world/documentation/gd004 8 2014 -- 11 -- 20 ectancy from different sources. The main sources used: g sion / United Nations Population Division, with projections C Riley on (link below) 50, a constructed, simple model for showing levels and changes in historical life expectancy SET FOR STATISTICAL ANALYSIS TION FOR MORE DETAILS Mattias Lindgren, Klara Johansson Version 8: included WPP estimates for 2013. Added temporary guesstimates for Taiwan & Greenland for 2013 (these two guestimates are not inlcuded in the gapdata file). Version 7: added guesstimates for disasters etc. Changed what years to include. Version 6: updated to 2012 with World Population prospects, some backward revisions, added guesstimates for Haiti earthquake, Somali famine & Syrian war. Version 4: updated with the world population prospect 2010, with some additional backward revisions. No footnotes Not in use Link to graph_setings Name next to axis Link next to axis Scale type (log or lin) ... http://spreadsheets.google.com/pub? key=pk7kRzzfckbzz4AmH_e3DNA Various sources http://spreadsheets.google.com/pub?key=phAwcNAVuyj1jiMAkmq1iMg&gid=1 log NAVuyj1jiMAkmq1iMg&gid=1 INDICATO VERSION R_V2_EN