<jupyter_start><jupyter_text>Fundamentals of Data Analysis Practical Assignment  ANSCOMBE'S QUARTET DATASET*** 1. Explain the background to the dataset - who created it, when it was created, and any speculation you can find regarding how it might have been created.2. Plot the interesting aspects of the dataset.3. Calculate the descriptive statistics of the variables in the dataset.4. Explain why the dataset is interesting, referring to the plots and statistics above.***  1.  Explain the background to the dataset - who created it, when it was created, and any speculation you can find regarding how it might have been created.*** Anscombe's quartet dataset are four datasets that have almost the same descriptive statistics but when plotted as graphs, are completely different. The dataset was created by <NAME> in 1973.(1) I believe <NAME> created the dataset to show that relying on statistical summaries may be misleading and that we need to also visualize the results.  2. Plot the interesting aspects of the dataset.*** Based on my research so far, which has consisted of looking at countless numbers of websites, plots and youtube video on the subject of Anscombe's quartet, I would guess the plots of the four datasets will be completely different.<jupyter_code>## import the libraries that will be used for plotting and descriptive statistics
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import csv
### magic function. With this magic function, the output of plotting commands is displayed inline within frontends like the <br>
### Jupyter notebook, directly below the code cell that produced it.The resulting plots will then also be stored in the <br>
### notebook document.(2)
%matplotlib inline
### csv files were created for each dataset and use the open function to open each file.
y = open("AnscombeI.csv") 
z = open("AnscombeII.csv")
a = open("AnscombeIII.csv")
b = open("AnscombeIV.csv")
### use the read function to read each of the dataset csv files and reads the dataset into the dataframe.
csv_y = csv.reader(y)
csv_z = csv.reader(z)
csv_a = csv.reader(a)
csv_b = csv.reader(b)
AI = pd.read_csv("AnscombeI.csv")
AII = pd.read_csv("AnscombeII.csv")
AIII = pd.read_csv("AnscombeIII.csv")
AIV = pd.read_csv("AnscombeIV.csv")
### use seaborn and matplotlib libraries to run the graphs of each of the 4 datasets.
sns.lmplot(x="x", y="y", data = AI,)
plt.title('Anscombe Dataset I')

sns.lmplot(x="x", y="y", data = AII)
plt.title('Anscombe Dataset II')

sns.lmplot(x="x", y="y", data = AIII)
plt.title('Anscombe Dataset III')

sns.lmplot(x="x", y="y", data = AIV)
plt.title('Anscombe Dataset IV')<jupyter_output><empty_output><jupyter_text>As predicted, the graphs are extremely different. Now let's look at the descriptive statistics.  3. Calculate the descriptive statistics of the variables in the dataset. ***<jupyter_code>### use the .describe function to list the important statistical summaries for each of the datasets.
print("\n"*1)
print("AnscombeI")
print(AI.describe())
print("\n"*1)
print("AnscombeII")
print(AII.describe())
print("\n"*1)
print("AnscombeIII")
print(AIII.describe())
print("\n"*1)
print("AnscombeIV")
print(AIV.describe())<jupyter_output>AnscombeI
               x          y
count  11.000000  11.000000
mean    9.000000   7.500909
std     3.316625   2.031568
min     4.000000   4.260000
25%     6.500000   6.315000
50%     9.000000   7.580000
75%    11.500000   8.570000
max    14.000000  10.840000


AnscombeII
               x          y
count  11.000000  11.000000
mean    9.000000   7.500909
std     3.316625   2.031657
min     4.000000   3.100000
25%     6.500000   6.695000
50%     9.000000   8.140000
75%    11.500000   8.950000
max    14.000000   9.260000


AnscombeIII
               x          y
count  11.000000  11.000000
mean    9.000000   7.500000
std     3.316625   2.030424
min     4.000000   5.390000
25%     6.500000   6.250000
50%     9.000000   7.110000
75%    11.500000   7.980000
max    14.000000  12.740000


AnscombeIV
               x          y
count  11.000000  11.000000
mean    9.000000   7.500909
std     3.316625   2.030579
min     8.000000   5.250000
25%     8.000000   6.170000
50%     8.000000   7.040[...]
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