How to figure out whether grossnationalincome is interval scaled
or not by looking at the data below.
Homework Answers
The interval scale of measurement is a type of measurement scale that is characterized by equal intervals between scale units.
Example of an interval scale is the Fahrenheit scale to measure temperature. The scale is made up of equal temperature units so that the difference between 40 and 50 degrees Fahrenheit is equal to the difference between 50 and 60 degrees Fahrenheit.
With an interval scale, you know not only whether different values are bigger or smaller, but you also know how much bigger or smaller they are. For example, suppose it is 60 degrees Fahrenheit on Monday and 70 degrees on Tuesday. You know not only that it was hotter on Tuesday, but you also know that it was 10 degrees hotter.
Gross national income is an interval scale as we know 1000 is smaller than 2000 and there is a difference of 1000 between them. Each $ 100 difference in the income is the same in every country.
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How to figure out whether grossnationalincome is interval scaled
or not by looking at the data...
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...
Philosophy of Business
Underdeveloped Countries 2020An underdeveloped country is a country characterized by chronic widespread poverty and less economic development than other nations. Emerging markets, developing countries, and newly industrialized countries are terms that are often used interchangeably for an underdeveloped country.These countries have very low per capita income and many residents live in very poor conditions, including lacking access to education and health care. Additionally, underdeveloped countries have obsolete methods of production and social organization. These nations often experience high birth rates...
8 AutoSave D The Home Data Review View Help Power Pivot Formulas Insert Draw Page Layout...
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...
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...
ccode country lrgdppc proc_exp malaria lsettlr_mort AGO Angola 7.77 5.36 1 5.63479 ARG Argentina 9.13 6.39 0 4.232656 AUS Australia 9.9 9.32 0 2.145931 BFA Burkina Faso 6.85 4.45 1 5.63479...
ccode country lrgdppc proc_exp malaria lsettlr_mort AGO Angola 7.77 5.36 1 5.63479 ARG Argentina 9.13 6.39 0 4.232656 AUS Australia 9.9 9.32 0 2.145931 BFA Burkina Faso 6.85 4.45 1 5.63479 BGD Bangladesh 6.88 5.14 0.158 4.268438 BOL Bolivia 7.93 5.64 0.00528 4.26268 BRA Brazil 8.73 7.91 0.1935 4.26268 CAN Canada 9.99 9.73 0 2.778819 CHL Chile 9.34 7.82 0 4.232656 CIV Cote d'Ivoire 7.44 7 1 6.504288 CMR Cameroon 7.5 6.45 1 5.63479 COG Congo 7.42 4.68 1 5.480639...
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...
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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