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The function scipy.stats.linregress(x,y) performs a linear regression of y on s. It returns five values: the slope, intercept, correlation, p-value against the null hypothesis that the slope is zero, and the standard-error. You can extract the slope and intercept as follows: m,b,r,p,SE-scipy.stats.linregress(x,y) In this exercise we are only interested in finding m and b and making a plot of the line y=mx + b You can plot a data set of z points and y points as dots and the best fit line using plt.plot and plt.scatter as follows: m, b , r, p ,SE-scipy. stats. Inregress (x,y) plt.scatter(x,y) plt.plot (xmin,xmax], [m*xmin+b, m*xmax+b ]) Here xmin and xmax are the left and right endpoints of the line. The following table contains four data sets: The function scipy.stats.linregress(x,y) performs a linear regression of y on s. It returns five values: the slope, intercept, correlation, p-value against the null hypothesis that the slope is zero, and the standard-error. You can extract the slope and intercept as follows: m,b,r,p,SE-scipy.stats.linregress(x,y) In this exercise we are only interested in finding m and b and making a plot of the line y=mx + b You can plot a data set of z points and y points as dots and the best fit line using plt.plot and plt.scatter as follows: m, b , r, p ,SE-scipy. stats. Inregress (x,y) plt.scatter(x,y) plt.plot (xmin,xmax], [m*xmin+b, m*xmax+b ]) Here xmin and xmax are the left and right endpoints of the line. The following table contains four data sets