Statistical Analysis in Python

t-test 🧪

Independent t-test: Compares means between two groups.
from scipy.stats import ttest_ind group1 = [20, 21, 22, 23, 24] group2 = [25, 26, 27, 28, 29] t_stat, p_val = ttest_ind(group1, group2) print(t_stat, p_val)
Paired t-test: Compares means from the same group at different times.
from scipy.stats import ttest_rel before = [20, 21, 22, 23, 24] after = [25, 26, 27, 28, 29] t_stat, p_val = ttest_rel(before, after) print(t_stat, p_val)
One-sample t-test: Tests if the sample mean is different from a known value.
from scipy.stats import ttest_1samp sample = [20, 21, 22, 23, 24] t_stat, p_val = ttest_1samp(sample, 22) print(t_stat, p_val)

One-way ANOVA: Compares means across multiple groups.
from scipy.stats import f_oneway group1 = [20, 21, 22, 23, 24] group2 = [25, 26, 27, 28, 29] group3 = [30, 31, 32, 33, 34] f_stat, p_val = f_oneway(group1, group2, group3) print(f_stat, p_val)
Two-way ANOVA: Assesses the effect of two factors.
import statsmodels.api as sm from statsmodels.formula.api import ols data = {'value': [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], 'group1': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'C'], 'group2': ['X', 'X', 'Y', 'Y', 'X', 'X', 'Y', 'Y', 'X', 'Y']} df = pd.DataFrame(data) model = ols('value ~ C(group1) + C(group2) + C(group1):C(group2)', data=df).fit() anova_table = sm.stats.anova_lm(model, typ=2) print(anova_table)

Chi-Square Test: Tests relationship between categorical variables.
from scipy.stats import chi2_contingency table = [[10, 20, 30], [6, 9, 17], [8, 15, 20]] chi2, p, dof, expected = chi2_contingency(table) print(chi2, p)

Pearson correlation: Measures linear relationship.
from scipy.stats import pearsonr x = [1, 2, 3, 4, 5] y = [2, 3, 4, 5, 6] corr, p_val = pearsonr(x, y) print(corr, p_val)
Spearman correlation: Measures rank-order relationship.
from scipy.stats import spearmanr x = [1, 2, 3, 4, 5] y = [2, 3, 4, 5, 6] corr, p_val = spearmanr(x, y) print(corr, p_val)

Linear Regression: Models relationship between variables.
from sklearn.linear_model import LinearRegression X = [[1], [2], [3], [4], [5]] y = [2, 3, 4, 5, 6] model = LinearRegression() model.fit(X, y) print(model.coef_, model.intercept_)
Logistic Regression: Models binary outcomes.
from sklearn.linear_model import LogisticRegression X = [[1], [2], [3], [4], [ 5]] y = [0, 0, 1, 1, 1] model = LogisticRegression() model.fit(X, y) print(model.coef_, model.intercept_)

Mann-Whitney U test: Compares differences between two independent groups.
from scipy.stats import mannwhitneyu group1 = [1, 2, 3, 4, 5] group2 = [6, 7, 8, 9, 10] u_stat, p_val = mannwhitneyu(group1, group2) print(u_stat, p_val)
Wilcoxon signed-rank test: Compares differences between two related groups.
from scipy.stats import wilcoxon before = [20, 21, 22, 23, 24] after = [25, 26, 27, 28, 29] w_stat, p_val = wilcoxon(before, after) print(w_stat, p_val)