# 55. Statistics: Multi-comparison with Tukey’s test and the Holm-Bonferroni method

If an ANOVA test has identified that not all groups belong to the same population, then methods may be used to identify which groups are significantly different to each other.

Below are two commonly used methods: Tukey’s and Holm-Bonferroni.

These two methods assume that data is approximately normally distributed. Continue reading “55. Statistics: Multi-comparison with Tukey’s test and the Holm-Bonferroni method”

# 54. Statistics: Analysis of variance (ANOVA)

One way analysis of variance (ANOVA) tests whether multiple groups all belong to the same population or not.

If a conclusion is reached that the groups do not all belong to the same population, further tests may be utilised to identify the differences. Continue reading “54. Statistics: Analysis of variance (ANOVA)”

# 47. Linear regression with scipy.stats

``````%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

# Set up x any arrays

x=np.array([1,2,3,4,5,6,7,8,9,10])
y=np.array([2.3,4.5,5.0,8,11.1,10.9,13.9,15.4,18.2,19.5])
y=y+10

# scipy linear regression

gradient, intercept, r_value, p_value, std_err = stats.linregress(x,y)

# Calculated fitted y

y_fit=intercept + (x*gradient)

# Plot data

plt.plot(x, y, 'o', label='original data')
plt.plot(x, y_fit, 'r', label='fitted line')

# Add text box and legend

text='Intercept: %.1f\nslope: %.2f\nR-square: %.3f' %(intercept,gradient,r_value**2)
plt.text(6,15,text)
plt.legend()

# Display plot

plt.show() ```Linear regression with scipy.stats```