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1. Compute the range, interquartile range, variance, and standard deviation of the following 10 scores. Please...
Country Suicides/ 100,000 Guatemala 2.5 United Arab Emirates 2.9 Venezuela, RB 3 Algeria 3.1 Lebanon 3.1...
Country Suicides/ 100,000 Guatemala 2.5 United Arab Emirates 2.9 Venezuela, RB 3 Algeria 3.1 Lebanon 3.1 Myanmar 4.3 Greece 4.3 Mexico 5 Israel 5.5 Tunisia 5.5 Peru 5.8 Malta 6 Colombia 6.1 Brazil 6.3 Kenya 6.5 Ghana 6.9 Ecuador 7.5 Italy 7.9 Ethiopia 8.4 Spain 8.5 United Kingdom 8.5 Turkey 8.7 Burkina Faso 9.2 Nicaragua 9.5 China 10 Norway 10.9 Luxembourg 11.1 Ireland 11.7 Australia 11.8 New Zealand 11.9 Denmark 12.2 Canada 12.3 Netherlands 12.6 Iceland 13.1 Germany 13.4 Portugal...
HI, I need help with this question. Please answer in details. The data set is found below for eac...
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...
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...
Homework #1-Chapter1&2&4&5 (20pts for turned in on time) 7. (14pts)Carbon Dioxide Emissions. Burning fuels in power...
Homework #1-Chapter1&2&4&5 (20pts for turned in on time) 7. (14pts)Carbon Dioxide Emissions. Burning fuels in power plants or motor vehicles emits carbon dioxide (CO2), which contributes to global warming. Table below shows CO2 emissions per person from countries with populations of at least 20 million. Carbon Dioxide emissions (metric tons per person) CO2 Country Ukraine SouthAfrica Italy Poland CO2 Country Country CO2 6.3 1.6 Ethiopia 0.1 Indonesia 7 1.8 Nepal Tanzania 0.1 Brazil 7.8 2 0.1 Egypt 7.8 Congo 2.6...
22 Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country....
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...
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...
11.38 Building a multiple linear regression model. Let’s now build a model to predict the life-satisfaction...
11.38 Building a multiple linear regression model. Let’s now build a model to predict the life-satisfaction score, LSI. (a) Consider a simple linear regression using GINI as the explanatory variable. Run the regression and summarize the results. Be sure to check assumptions. (b) Now consider a model using GINI and LIFE. Run the multiple regression and summarize the results. Again be sure to check assumptions. (c) Now consider a model using GINI, LIFE, and DEMOCRACY. Run the multiple regression and...
DATA: # happy2.py import csv def main(): happy_dict = make_happy_dict() print_sorted_dictionary(happy_dict) def make_happy_dict(): filename = "happiness.csv"...
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...
Will reward thumbs up 100% if works. thank you Pickling with Python code and Pandas code Do both ...
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...
Homework #1-Chapter1&2&4&5 (20pts for turned in on time) 7. (14pts)Carbon Dioxide Emissions. Burning fuels in power plants or motor vehicles emits carbon dioxide (CO2), which contributes to global warming. Table below shows CO2 emissions per person from countries with populations of at least 20 million. Carbon Dioxide emissions (metric tons per person) CO2 Country Ukraine SouthAfrica Italy Poland CO2 Country Country CO2 6.3 1.6 Ethiopia 0.1 Indonesia 7 1.8 Nepal Tanzania 0.1 Brazil 7.8 2 0.1 Egypt 7.8 Congo 2.6...
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...
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