Country | Continent | GDP (millions of US$) |
Afghanistan | Asia | 18,181 |
Albania | Europe | 12,847 |
Algeria | Africa | 190,709 |
Angola | Africa | 100,948 |
Argentina | South America | 447,644 |
Australia | Oceania | 1,488,221 |
Austria | Europe | 419,243 |
Azerbaijan | Europe | 62,321 |
Bahrain | Asia | 26,108 |
Bangladesh | Asia | 113,032 |
Belarus | Europe | 55,483 |
Belgium | Europe | 513,396 |
Bolivia | Africa | 24,604 |
Bosnia and Herzegovina | Europe | 17,965 |
Botswana | Africa | 17,570 |
Brazil | South America | 2,492,908 |
Brunei | Asia | 15,533 |
Bulgaria | Europe | 53,514 |
Burma | Asia | 51,925 |
Cambodia | Asia | 12,861 |
Cameroon | Africa | 25,759 |
Canada | North America | 1,736,869 |
Chile | South America | 248,411 |
China | Asia | 7,298,147 |
Colombia | South America | 328,422 |
Congo, Democratic Republic of the | Africa | 15,668 |
Congo, Republic of the | Africa | 14,769 |
Costa Rica | North America | 40,947 |
Côte d'Ivoire | Africa | 24,096 |
Croatia | Europe | 63,842 |
Cyprus | Europe | 24,949 |
Czech Republic | Europe | 215,265 |
THE REST IS ON THE NEXT POSTS: Thank U!
Recall in Ch. 2, problem 2, you sorted and filtered the countries in GDPlist data. This time, do the following.
Will reward thumbs up 100% if works. thank you Pickling with Python code and Pandas code Do both pickling assignment in one Jupyter Notebook file. Python Pickle steps: Download the CSV file. Load into a Pandas DataFrame. Make the column ‘country’ the index. Print the header. Using Python code, pickle the DataFrame and name the file: PythonPickle. Load back the PythonPickle data into the DataFrame. Print the header. (Note both printed headers should match.) Pandas Pickle steps: Download the CSV...
HI, I need help with this question. Please answer in details. The data set is found below for each countries sugar consumption. Thanks! Country,Sugar, GDP, Continent Albania,15.3,4556.144342, Europe Argentina, 38.1,13693.70379, South America Armenia, 33.2,3421.704509, Europe Australia, 34.1, 62080.98242, Europe Austria, 37.9,49485.48219, Europe Azerbaijan,13.9,7189.691229, Europe Belarus,31.8,6305.773662, Europe Belgium, 41.4,46463.60378, Europe Bosnia and Herzegovina,13.4,4754.197861, Europe Brazil, 36.5,12576.19559, South America Canada, 31.3,51790.56695, North America Chile, 41.7,14510.9661, South America China, 6.2,5447.309378,Asia Colombia,23.2, 7124.54892, South America Czech Republic, 30.6,20584.92655, Europe Denmark, 38,59911.90466,Europe Egypt, 26.4,2972.583516,Africa Estonia,31.4,16982.30031,...
HI, I need help with answering these questions. Please explain and answer all parts. Data for all the countries and then the question at the bottom. Sugar Consumption Per Capita.csv Country Albania Argentina Armenia Australia Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina 13.4 4754.197861 Europe Brazil Canada Chile China Colombia Czech Republic Denmark Egypt Estonia Finland France Georgia Germany Ghana Greece Hungary Iceland India Indonesia Iran Sugar GDP Continent 15.3 4556.144342 Europe 38.1 13693.70379 South America 33.2 3421.704509 Europe 34.1...
QUESTION 2) What is the conclusion for t-test concerning if the mean child mortality rate for countries in Eastern Europe is more than the mean child mortality rate for countries in the Middle East? There is insufficient evidence that the mean child mortality rate for countries in Eastern Europe is more than the mean Child mortality rate for countries in the Middle East. The means are exactly the same. There is insufficient evidence that the mean child mortality rate for...
Case Description Corporations with international operations need to assess the risks associated with setting up and maintaining operations in different regions of the world. Consideration of the risks include considering such issues as political and economic stability. One indicator of the healthcare and quality of life in a country or region that is considered correlated with the risk and stability in the region is the child mortality rate. As a result, the healthcare and quality of care as measured by...
12 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp. 13 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent (color) 14 Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent and pop (color and size) 15 Get data for Europe in 2002. Call it data_Europe Looking for these problems in R command code answers. 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 142 142 142 142 142 142 142 142 142 142 142 142...
QUESTION 3)What is the critical value of the test statistic to test the mean child mortality rate for countries in Africa is more than the mean child mortality rate for countries in South Asia? 0.59 0.299 0.126 2.015 2.35 Case Description Corporations with international operations need to assess the risks associated with setting up and maintaining operations in different regions of the world. Consideration of the risks include considering such issues as political and economic stability. One indicator of the...
ONLY ANSWER THE QUESTIONS AT THE BOTTOM Case Description Corporations with international operations need to assess the risks associated with setting up and maintaining operations in different regions of the world. Consideration of the risks include considering such issues as political and economic stability. One indicator of the healthcare and quality of life in a country or region that is considered correlated with the risk and stability in the region is the child mortality rate. As a result, the healthcare...
22 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country. 23 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country and size by pop. Looking for the answers in R command codes. 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 142 142 142 142 142 142 142 142 142 142 142 142 > table(gapminder$country) Afghanistan Albania Algeria 12 12 12 Angola Argentina Australia 12...
8 AutoSave D The Home Data Review View Help Power Pivot Formulas Insert Draw Page Layout ES - per capita GDP 1 Country Name 2 Central African Republic 3 Myanmar 4 Congo, Dem. Rep. 5 South Sudan 6 Madagascar 7 Burundi 8 Ethiopia 9 Guinea 10 Malawi 11 Niger 12 Gambia, The 13 Bangladesh 14 Guinea-Bissau 15 Lao PDR 16 Benin 17 Pakistan 18 Chad 19 Nepal 20 Mozambique 21 uberia 22 Kenya 23 Senegal 24 Burkina Faso 25 Mauritania...