(A). Nigeria: The population will grow rapidly, as we can see that it has high population of pre-productive/reproductive ages compared to the post-reproductive ages.
US: The population will grow, yes but slowly, as we can see that it has high population of pre-productive/reproductive ages compared to the post-reproductive ages. This population growth will be less than Nigeria.
Japan: There will not much overall difference in the japan population, as we can see the pre-productive/reproductive people are almost equal to post-reprodcutive group of people.
Germany: The overall population will decrease as we can see the post-reproductive group goes way ahead when compared to pre-reproductive/reproductive ages.
(B): when we talk of societal needs, what we need for basic living is food, water, health, job, housing and clothing, security, and transport.
so according to this:
Nigeria & US, would need increase food and water productivity as they will have a increase in young population which will need more food and water. Increase in food production will also give jobs which will be needed. The health industry also upgraded for this type of young population. This huge population will need jobs, and for job they need transport, so it will also be upgraded.
Japan: As the population would be near about stable then existing policies would be fair enough for the future of the country. But as a politician they need to update the policies for future technology.
Germany; it has to be invest heavily in health sector because in long run their will be increased aged population, also they had to invest in awareness for a balanced society towards educating young people in their reproductive age group to make families. Financial assistance can be provided to reproductive age group people so that the country can have a younger population.
Please help! 5. 12.5 pts) Study the age structures below and answer the following questions. Male...
Review the
culture index power point slides in the additional power point
slides folder. Answer the following 4 questions.
1) Describe
the major components of the Lewis Model?
2) How could
you utilize this model in your sourcing activities and engagement
with other professionals?
3) How does
the global mindset differ from the domestic mindset?
4) To bridge a
cultural gap how would you address the 7 cultural dimensions at
play?
Week 2-20 × | p Ginuwine Ra xle Get...
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,...
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...
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...
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...
DATA:
# happy2.py
import csv
def main():
happy_dict = make_happy_dict()
print_sorted_dictionary(happy_dict)
def make_happy_dict():
filename = "happiness.csv"
happy_dict={}
with open(filename, 'r') as infile:
csv_happy = csv.reader(infile)
infile.readline()
for line in csv_happy:
happy_dict[line[0]] = line[2]
return happy_dict
def lookup_happiness_by_country(happy_dict):
return
def print_sorted_dictionary(D):
if type(D) != type({}):
print("Dictionary not found")
return
print("Contents of dictionary sorted by key.")
print("Key","Value")
for key in sorted(D.keys()):
print(key, D[key])
main()
"happines.csv"
Country,Year of Estimate,Happiness Index
Afghanistan,2018,2.694303274
Albania,2018,5.004402637
Algeria,2018,5.043086052
Angola,2014,3.794837952
Argentina,2018,5.792796612
Armenia,2018,5.062448502
Australia,2018,7.17699337
Austria,2018,7.396001816
Azerbaijan,2018,5.167995453
Bahrain,2017,6.227320671
Bangladesh,2018,4.499217033...
Please help me answer theses practice questions
QUESTION 2 Which of the following can a country implement to protect local industries (e.g. bicycles) according to the video on the deceptive promise of free trade? Border walls local training programs to strengthen local industries protectionist policies such as tarrifs creating a high minimum wage locally governments can't do anything QUESTION 3 Which of the following European countries has a trade surpluse with the US as well as most other European countries...
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...