linearreg.py

importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main() 
importnumpyasnp
importmatplotlib.pyplotasplt

defestimate_coef(x, y): 
# number of observations/points 
n=np.size(x) 

# mean of x and y vector 
m_x, m_y=np.mean(x), np.mean(y) 

# calculating cross-deviation and deviation about x 
SS_xy=np.sum(y*x) -n*m_y*m_x
SS_xx=np.sum(x*x) -n*m_x*m_x

# calculating regression coefficients 
b_1=SS_xy/SS_xx
b_0=m_y-b_1*m_x

return(b_0, b_1) 

defplot_regression_line(x, y, b): 
# plotting the actual points as scatter plot 
plt.scatter(x, y, color="m", marker="o", s=30)

# predicted response vector 
y_pred=b[0] +b[1]*x

print("pred value of ", y," is ", y_pred)
print("error at ",y," is," ,y-y_pred)

# plotting the regression line 
plt.plot(x, y_pred, color="g") 

# putting labels 
plt.xlabel('x') 
plt.ylabel('y') 

# function to show plot 
plt.show() 

defmain(): 
# observations 
x=np.array([5,7,10,15,23]) 
y=np.array([10,20,30,40,50]) 

# estimating coefficients 
b=estimate_coef(x, y) 
print("Estimated coefficients:\nb_0 = {} \nb_1 = {}".format(b[0], b[1])) 

# plotting regression line 
plot_regression_line(x, y, b) 

if__name__=="__main__": 
main()