Math 200 - Project 1 This project will cover topics from chapters 1 through 4 of your textbook. All papers will need to be submitted in IvyLearn. You will be performing an analysis on a dataset that contains data on fertility and life expectancy for 191 different countries. All data is from the year 2019. The fertility numbers are the average number of children per woman in each of the countries. The life expectancy numbers are the average life expectancy in each of the countries. You will be turning in a paper that should include section headings, graphics and tables when appropriate and complete sentences which explain all analysis, conclusions, and results. There is not a specified length, however it is important that you follow all steps below and grade yourself using the rubric provided since it is the rubric that will be used to grade your submissions. All work should be your own. Plagiarism will result in a project score of 0. Steps (all statistical analysis to be done in Excel and/or StatCrunch): 1. Watch the TED talk by Hans Roling titled "The best stats you've ever seen". You will need to include comments on this in your paper. Here is a link: https://www.youtube.com/watch?v=usdJgEwMinM 2. Download the Excel data from IvyLearn 3. Create histograms of each of the variables (one histogram for fertility, one for life expectancy). Use the histograms to identify the shapes of the distribution. StatCrunch will be the easier tool to use for this particular task. If you choose to use Excel, you can use the "pivot table" function and create histograms from there. 4. Calculate some descriptive statistics for each of the variables, including but not limited to the mean, median and standard deviation. Organize these numbers nicely in a table. Then write a short paragraph using/reporting/describing your statistics. 5. Using fertility as the predictor variable and life expectancy as the response variable, create a scatter diagram, come up with the least-squares regression line (you need to state the actual equation for the least-squares regression line) and calculate the linear correlation coefficient as well as the coefficient of determination. Make sure that you understand all interpretations and include them in your paper. Please carefully review the rubric below to see the full list of required interpretations. 6. Use the regression line to predict life expectancy for countries with the fertility rate like that of the United States. Compare this to the actual value in the United States. Hint: you will have to plug the actual fertility value for the US into your equation. Interpret your results. Hint: Is the US above or below the average given its fertility rate? 7. Name some possible lurking variables that may be at work here.

Math 200 - Project 1 8. Explain the difference between correlation and causation and why we cannot say that there is a cause and effect relationship in this situation. 9. Explain why we cannot use our regression model to predict the life expectancy of one particular individual. 10. Take a look at the website where this data was pulled from and comment on how the model might have been different if we used the data from 20, 40 or 60 years ago. Navigate to http://gapminder.org and click on "Resources". Scroll to the "Tools" section and select "Bubble Chart". You should see a bubble chart appear. Use the x-axis and y-axis dropdown menus to ensure that 'life expectancy (years)' is selected on the y-axis and 'children per woman (total fertility)' is selected on the x-axis. Once selected, play the video to see the changes over time and comment. 11. Put everything together into an organized paper and submit on IvyLearn.

Country 2019 Fertility2019 Life Expectancy Afghanistan 4.3 65.98 Albania 1.6 78.96 Algeria 3 77.5 Angola 5.4. 62.22 Antigua and Barbuda 2 77.47 Argentina 2.2 77.17 Armenia 1.8 75.55 Aruba 1.9 76.79 Australia 1.7 83.94 Austria 1.5 82.05 Azerbaijan 1.8 73.33 Bahamas 1.7 74.28 Bahrain 2 77.73 Bangladesh 2. 73.57 Barbados 1.6 79.64 Belarus 1.4 75.2 Belgium 1.6. 82.17 Belize 2.3. 75.09 Benin 4.8 62.84 Bhutan 2.9 72.77 Bolivia 2.7 72.35 Bosnia and Herzegovina 1.3 77.93 Botswana 2.8 69.86 Brazil 1.7 75.57 Brunei 1.8 76.35 Bulgaria 1.6 75.49 Burkina Faso 5.1. 62.98 Burundi 5.3 62.71 Cambodia 2.5 70.54 Cameroon 4.5 60.32 Canada 1.5 82.96 Cape Verde 2.2 73.58 Chad 5.6 55.17 Channel Islands 1.5 83.6 Chile 1.6. 80.74 China 1.7 77.47 Colombia 1.8 77.87 Comoros 4.1 65.03 Congo, Dem. Rep. 5.8 61.6 Congo, Rep. 4.4 65.21 Costa Rica 1.7. 80.94 Cote d'Ivoire 4.6 58.75 Croatia 1.5 79.02 Cuba 1.6 79.18 Cyprus 1.3 81.51 Czech Rep. 1.7 79.85 Denmark 1.7. 81.4 Djibouti 2.7 67.87 Dominican Rep. 2.3 74.65 Ecuador 2.4 77.71 Egypt 3.3 72.54 El Salvador 2 74.06 Equatorial Guinea 4.4 59.82 Eritrea 4 67.48 Estonia 1.7 79.18 Ethiopia 4.1 67.81 Fiji 2.8 67.91 Finland 1.4 82.48 France 1.9 83.13 French Guiana 4.4 80.53 French Polynesia 1.9 78.23 Gabon 3.9 67.03 Gambia 5.2 63.26 Georgia 2.1 74.24 Germany 1.5 81.88 Ghana 3.8 64.94 Greece 1.4. 82.8 Greenland 2.1 72.9 Grenada 2 72.59 Guadeloupe 2.8 82.74 Guam 2.3 80.74 Guatemala 2.8 75.05 Guinea 4.6 62.64 Guyana 2.4 70.26 Haiti 2.9 64.99 Honduras 2.4 75.87 Hong Kong, China 1.1 85.29 Hungary 1.5 77.31 Iceland 1.8 83.52 India 2.2 70.42 Indonesia 2.3 72.32 Iran 2.1 77.33 Iraq 3.6 71.08 Ireland 1.7 82.81 Israel 3 83.49 Italy 1.3 84.01 Jamaica 2 74.88 Japan 1.4 85.03 Jordan 2.7 75.01 Kazakhstan 2.9 73.9 Kenya 3.4 67.47 Kiribati 3.5 69.17 Korea, Dem. Rep. 1.9 83.5 Korea, Rep. 0.9 72.89 Kuwait 2.1 75.85 Kyrgyzstan 3.3 71.95 Laos 2.6. 68.89 Latvia 1.6 75.73 Lebanon 2.1 79.27 Liberia 4.2 65 Libya 2.2 76.46 Lithuania 1.6 76.41 Luxembourg 1.3 82.79 Macao, China 1.2 84.68 Macedonia, FYR 1.5 76.26 Madagascar 4 68.21 Malawi 4.1 65.62 Malaysia 2 76.65 Maldives 1.8 79.89 Mali 5.8. 60.54 Malta 1.1. 83.06 Mauritania 4.5 65.57 Mexico 2.1 75.41 Micronesia, Fed. Sts. 3 74.08 Moldova 1.3 72.3 Mongolia 2.9 70.53 Montenegro 1.7 77.39 Morocco 2.4 77.43 Mozambique 4.8 62.13 Myanmar 2.1 67.78 Namibia 3.3 64.86 Nepal 1.9 71.75 Netherlands 1.6 82.78 New Caledonia 1.9 78.16 New Zealand 1.7 82.8 Nicaragua 2.4 75.23 Niger 6.8 63.62 Nigeria 5.3 55.75 Norway 1.5 82.94 Oman 2.8 78.58 Pakistan 3.5 67.79 Panama 2.4 79.1 Papua New Guinea 3.5 65.22 Paraguay 2.4 74.59 Peru 2.2 77.44 Philippines 2.5 71.66 Poland 1.4 79.27 Portugal 1.4 82.65 Puerto Rico 1 80.69 Qatar 1.8 80.73 Romania 1.8 76.5 Russia 1.5 72.99 Rwanda 4 70 Samoa 3.8. 73.75 Sao Tome and Principe 4.3 71.01 Saudi Arabia 2.3. 75.69 Senegal 4.6 68.87 Serbia 1.5 76.47 Seychelles 2.3 73.74 Sierra Leone 4.2 55.92 Singapore 1.1 84.07 Slovak Republic 1.6 78 Slovenia 1.6 81.85 Solomon Islands 4.4 73.38 Somalia 6 58.34 South Africa 2.4 64.88 South Sudan 4.6. 58.74 Spain 1.2. 83.99 Sri Lanka 2.2 77.56 Sudan 4.3 66.09 Suriname 2.4. 73.13 Sweden 1.7 83.33 Switzerland 1.5. 84.25 Syria 2.8 76.06 Taiwan 1.167 81.04 Tajikistan 3.6 71.76 Tanzania 4.8 66.39 Thailand 1.5 77.74 Timor-Leste 3.9 70.18 Togo 4.3. 62.13 Tonga 3.5 71.32 Trinidad and Tobago 1.7 73.91 Tunisia 2.2 77.36 Turkey 2.1 78.45 Turkmenistan 2.7 68.63 Uganda 4.8 64.38 Ukraine 1.2 72.5 United Arab Emirates 1.4 78.46 United Kingdom 1.6 81.77 United States 1.7 79.11 Uruguay 2 78.43 Uzbekistan 2.8 72.04 Vanuatu 3.7 70.99 Venezuela 2.3. 72.34 Vietnam 2 57.77 Virgin Islands (U.S.) 2. 81.17 West Bank and Gaza 3.6 74.05 Western Sahara 2.56 71.08 Yemen, Rep. 3.7 66.44 Zambia 4.6 64.7 Zimbabwe 3.5 62.16

I intend to submit tutor's work as my own