1. Get the r value and the p value between the dataset:

　　r_fta_pts,p_value = http://www.mamicode.com/pearsonr(nba_stats["pts"],nba_stats["fta"]) 

　　r_stl_pf,p_value = http://www.mamicode.com/pearsonr(nba_stats["stl"],nba_stats["pf"]) # It will return R value and P value.

2. The function of getting convariance form two data set, the convariance is the value that measure how much two variables correlated with each other. If one changes to bigger, the other changes to bigger. which said these two variables are corresponse. Here is the function of getting the convariance:

here is the formular:

　　def conv_compute(x,y): #define a function to calculate the convariance

　　mean_x = sum(x)/len(x)
　　mean_y = sum(y)/len(y)# calculate the mean of each column
　　x_diff = [i-mean_x for i in x]
　　y_diff = [n-mean_y for n in y] # calculate the difference for both column, if it is hard to use for loop, we can think about the list function.
　　sum_diff =[x_diff[i]* y_diff[i] for i in range(len(x))] # use range(len()) function to replace the for loop
　　return sum(sum_diff)/len(sum_diff)

　　cov_stl_pf = conv_compute(nba_stats["stl"],nba_stats["pf"])
　　cov_fta_pts = conv_compute(nba_stats["fta"],nba_stats["pts"])

3. The way to calculate correlation coefficient: The fomular is 

　　from numpy import cov

　　cov_1 = cov(nba_stats["fta"],nba_stats["blk"])[0,1]
　　std_1 = nba_stats["fta"].std() * nba_stats["blk"].std()
　　r_fta_blk = cov_1/std_1

Statistics and Linear Algebra 3