how to print equation of line using scipy stats

My favorite, given no other constraints is an arrow to the line, because then the reader has no doubt what the equation is actually referring to. To do this, use annotate:

x0 = 20000
y0 = slope * x0 + intercept
pylab.annotate(line_eqn, xy = (x0, y0), xytext = (x0 - .4 * x0, y0 + .4 * y0),
   arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=-0.5'))

Suggestion : 2

Defines the alternative hypothesis. Default is ‘two-sided’. The following options are available:,Calculate a linear least-squares regression for two sets of measurements.,The p-value for a hypothesis test whose null hypothesis is that the slope is zero, using Wald Test with t-distribution of the test statistic. See alternative above for alternative hypotheses.,The Pearson correlation coefficient. The square of rvalue is equal to the coefficient of determination.

slope, intercept, r, p, se = linregress(x, y)

result = linregress(x, y)
print(result.intercept, result.intercept_stderr)

>>>
import matplotlib.pyplot as plt >>>
   from scipy
import stats
   >>>
   rng = np.random.default_rng()

>>> x = rng.random(10) >>>
   y = 1.6 * x + rng.random(10)

>>> res = stats.linregress(x, y)

>>> print(f "R-squared: {res.rvalue**2:.6f}")
R - squared: 0.717533

Suggestion : 3

My code performs a linear regression on 2 sets of data. It works fine but i do not know how i can print the equation of the line onto the graph itself with scipy or numpy.,Python – How to do a line break (line continuation) in Python,Where do you want the equation to go? To put it on the title, for example: plt.title('$y=%3.7sx+%3.7s$'%(slope, intercept)). To put it inside the plot use plot.text.,Python – How to print literal curly-brace characters in a string and also use .format on it

Here is my code:

y = np.array([15, 1489, 859, 336, 277, 265, 229, 285, 391, 372, 5, 345])
x = np.array([196.16, 17762.47, 28542.19, 30170.5, 9384.06, 43210.29, 21819.2, 16978.2, 45767.54, 12328.78, 113.71, 19257.6])

print x
print y

slope, intercept, r_value, p_value, slope_std_error = stats.linregress(x, y)
print "slope = " + str(slope)
print "r_value = " + str(r_value)
print "r_squared = " + str(r_value ** 2)
print "p_value = " + str(p_value)
# Calculate some additional outputs
predict_y = intercept + slope * x
print predict_y
pred_error = y - predict_y
degrees_of_freedom = len(x) - 2
residual_std_error = np.sqrt(np.sum(pred_error ** 2) / degrees_of_freedom)

# Plotting
pylab.xlabel('cost')
pylab.ylabel('signups')
pylab.plot(x, y, 'o')
pylab.plot(x, predict_y, 'k-')
pylab.show()

Suggestion : 4

Last Updated : 20 Jun, 2022

Output :

Kurtosis
for normal distribution: -0.3073930877422071

Kurtosis
for normal distribution: 2.692606912257793

Kurtosis
for normal distribution: -0.3073930877422071

Suggestion : 5

Calculating with python the slope and the intercept of a straight line from two points (x1,y1) and (x2,y2):,Using s simple regression with scipy:,Plot with matplotlib:,I have developed this web site from scratch with Django to share with everyone my notes. If you have any ideas or suggestions to improve the site, let me know ! (you can contact me using the form in the welcome page). Thanks!

Calculating with python the slope and the intercept of a straight line from two points (x1,y1) and (x2,y2):

x1 = 2.0
y1 = 3.0

x2 = 6.0
y2 = 5.0

a = (y2 - y1) / (x2 - x1)
b = y1 - a * x1

print('slope: ', a)
print('intercept: ', b)

Using a function

def slope_intercept(x1, y1, x2, y2):
   a = (y2 - y1) / (x2 - x1)
b = y1 - a * x1
return a, b

print(slope_intercept(x1, y1, x2, y2))

Using s simple regression with scipy:

from scipy.stats
import linregress

slope, intercept, r_value, p_value, std_err = linregress([x1, x2], [y1, y2])
print(slope, intercept)