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 Values X is
∑ = 1994.9
Mean = 26.599
∑(X - Mx)2 = SSx = 8258.01
GDP Values Y is
∑ = 1626334.401
Mean = 21684.459
∑(Y - My)2 = SSy =
42290592466.331
X and Y Combined
N = 75
∑(X - Mx)(Y - My) = 5615579.317
R Calculation
Correlation r = ∑((X - My)(Y - Mx)) /
√((SSx)(SSy))
Correlation r = 5615579.317 / √((8258.01)(42290592466.331)) =
0.3005
Meta Numerics (cross-check)
Correlation r = 0.3005
Although technically a positive correlation, the relationship between your variables is weak (nb. the nearer the value is to zero, the weaker the relationship).
Click here to calculate a p-value.
The value of R2, the coefficient of determination, is 0.0903.
HI, I need help with answering these questions. Please explain and answer all parts. Data for all...
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,...
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...
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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