STEP 1

import numpy as np

import pandas as pd

import scipy.stats as st

import matplotlib.pyplot as plt

from IPython.display import display, HTML

# dataframe for this project

nba_wins_df = pd.read_csv('nba_wins_data.csv')

display(HTML(nba_wins_df.head().to_html()))

print("printed only the first five observations...")

print("Number of rows in the dataset =", len(nba_wins_df))

year_idfran_idavg_ptsavg_opp_pts avg_elo_n avg_opp_elo_navg_pts_differential avg_elo_differential total_wins

0 1995 Bucks 99.341463 103.707317 1368.604789 1497.311587 -4.365854 -128.706798 34

1 1995 Bulls 101.524390 96.695122 1569.892129 1488.199352 4.829268 81.692777 47

2 1995 Cavaliers 90.451220 89.829268 1542.433391 1498.848261 0.621951 43.585130 43

3 1995 Celtics 102.780488 104.658537 1431.307532 1495.936224 -1.878049 -64.628693 35

4 1995 Clippers 96.670732 105.829268 1309.053701 1517.260260 -9.158537 -208.206558 17

printed only the first five observations...

Number of rows in the dataset = 618

Step 2

import scipy.stats as st

# ---- TODO: make your edits here ----

plt.plot(nba_wins_df['avg_pts'], nba_wins_df['total_wins'], 'o')

plt.title('Total Number of Wins by Average Points Scored', fontsize=20)

plt.xlabel('Average Points Scored')

plt.ylabel('Total Number of Wins')

plt.show()

# ---- TODO: make your edits here ----

correlation_coefficient, p_value = st.pearsonr(nba_wins_df['avg_pts'], nba_wins_df['total_wins'])

print("Correlation between Average Points Scored and the Total Number of Wins ")

print("Pearson Correlation Coefficient =", round(correlation_coefficient,4))

print("P-value =", round(p_value,4))