Your coach expects teams to win more games in a regular season if they have a high average number of points compared to their opponents. This is because the chances of winning are higher if a team scores high in its games. Therefore, it is expected that the total number of wins and the average points scored are correlated. Calculate the Pearson correlation coefficient and its P-value. Make the following edits to the code block below: Replace ??DATAFRAME_NAME?? with the name of the dataframe used in this project. See Step 1 for the name of dataframe used in this project.
Replace ??POINTS?? with the name of the variable for average points scored in a regular season. See the table included in the Project Three instructions above to pick the variable name. Enclose this variable in single quotes. For example, if the variable name is var1 then replace ??POINTS?? with 'var1'.
Replace ??WINS?? with the name of the variable for the total number of wins in a regular season. Remember to enclose the variable in single quotes. See the table included in the Project Three instructions above to pick the variable name. Enclose this variable in single quotes. For example, if the variable name is var2 then replace ??WINS?? with 'var2'.
The code block below will print a scatterplot of the total number of wins against the average points scored in a regular season.
After you are done with your edits, click the block of code below and hit the Run button above.
In [8]:
import scipy. stats as st
# TODO: make your edits here
plt. plot('nba_wins_data. csv'['avg_pts'], 'nba_wins_data. csv'['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()