Statsmodels Stats

Statsmodels Statistical Analysis

Statistical modeling, hypothesis testing, and time series analysis using statsmodels and pandas.

When to Use

• Linear regression (OLS, GLS, WLS, robust)
• Logistic regression and generalized linear models
• Hypothesis testing (t-tests, ANOVA, chi-squared)
• Time series analysis (ARIMA, VAR, seasonal decomposition)
• Survival analysis and diagnostic plots

When NOT to Use

• Machine learning classification/regression (use scikit-learn)
• Deep learning or neural networks (use PyTorch/TensorFlow)
• Simple descriptive statistics only (use scipy-analysis)

OLS / GLS / WLS Regression

import statsmodels.api as sm
import statsmodels.formula.api as smf

model = smf.ols('y ~ x1 + x2 + x1:x2', data=df).fit()
print(model.summary())

# Matrix interface
X = sm.add_constant(df[['x1', 'x2']])
model = sm.OLS(df['y'], X).fit()

# WLS for heteroscedasticity
model_wls = sm.WLS(df['y'], X, weights=1.0/df['variance']).fit()

# Robust regression
model_rlm = sm.RLM(df['y'], X, M=sm.robust.norms.HuberT()).fit()

Logistic Regression

model = smf.logit('outcome ~ age + treatment', data=df).fit()
print(model.summary())
odds_ratios = np.exp(model.params)
conf_int = np.exp(model.conf_int())
mfx = model.get_margeff()
print(mfx.summary())

Hypothesis Testing

from statsmodels.stats.anova import anova_lm
from statsmodels.stats import weightstats, proportion

# t-test
t_stat, p_val, df_val = weightstats.ttest_ind(group_a, group_b)

# One-way ANOVA
model = smf.ols('value ~ C(group)', data=df).fit()
print(anova_lm(model, typ=2))

# Two-way ANOVA
model = smf.ols('value ~ C(factor_a) * C(factor_b)', data=df).fit()
print(anova_lm(model, typ=2))

# Proportion z-test
z_stat, p_val = proportion.proportions_ztest(count=[45, 60], nobs=[100, 120])

Time Series Analysis

# ARIMA
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(series, order=(p, d, q)).fit()
forecast = model.forecast(steps=12)

# Seasonal ARIMA (SARIMAX)
from statsmodels.tsa.statespace.sarimax import SARIMAX
model = SARIMAX(series, order=(1,1,1), seasonal_order=(1,1,1,12)).fit()
forecast = model.get_forecast(steps=24)

# VAR (vector autoregression)
from statsmodels.tsa.api import VAR
model = VAR(multivariate_df).fit(maxlags=5, ic='aic')

# Stationarity tests
from statsmodels.tsa.stattools import adfuller
adf_result = adfuller(series)
print(f'ADF Statistic: {adf_result[0]:.4f}, p-value: {adf_result[1]:.4f}')

Survival Analysis

from statsmodels.duration.hazard_regression import PHReg
model = PHReg(df['time'], df[['age', 'treatment']], status=df['event']).fit()
print(model.summary())

Diagnostic Plots

import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt

fig = sm.qqplot(model.resid, line='45')
fig.savefig('qqplot.png', dpi=150, bbox_inches='tight')
plt.close(fig)

fig, ax = plt.subplots()
sm.graphics.influence_plot(model, ax=ax)
fig.savefig('influence.png', dpi=150, bbox_inches='tight')
plt.close(fig)

# Heteroscedasticity test
from statsmodels.stats.diagnostic import het_breuschpagan
bp_stat, bp_p, _, _ = het_breuschpagan(model.resid, model.model.exog)

Statistical Rigor Standards

Every statistical result MUST include: 1. Test name and type 2. Test statistic value 3. p-value (exact, not "p < 0.05") 4. Effect size (Cohen's d, odds ratio, R-squared, etc.) 5. 95% confidence interval 6. Sample size (n per group)

Before interpreting any test:

• Verify assumptions (normality: Shapiro-Wilk; homoscedasticity: Levene/Breusch-Pagan; independence)
• If assumptions violated, use non-parametric alternatives or robust methods
• For multiple comparisons, apply FDR (Benjamini-Hochberg) or Bonferroni correction

Reporting standards:

• A significant p-value with a tiny effect size is NOT meaningful — always report both
• Distinguish correlation from causation explicitly
• Report negative results honestly — absence of effect is a finding, not a failure
• Never report p = 0.000; use scientific notation (e.g., p = 2.3e-7)

# Template for proper statistical reporting
def report_ttest(group_a, group_b, label_a="Group A", label_b="Group B"):
    from scipy import stats
    import numpy as np
    t, p = stats.ttest_ind(group_a, group_b)
    d = (np.mean(group_a) - np.mean(group_b)) / np.sqrt((np.std(group_a)**2 + np.std(group_b)**2) / 2)
    ci = stats.t.interval(0.95, len(group_a)+len(group_b)-2,
                          loc=np.mean(group_a)-np.mean(group_b),
                          scale=stats.sem(np.concatenate([group_a, group_b])))
    print(f"Independent t-test: t({len(group_a)+len(group_b)-2}) = {t:.3f}, "
          f"p = {p:.2e}, Cohen's d = {d:.3f}, 95% CI [{ci[0]:.3f}, {ci[1]:.3f}], "
          f"n = {len(group_a)} vs {len(group_b)}")

Best Practices

1. Always check model assumptions before interpreting results. 2. Use model.summary() for comprehensive fit statistics. 3. Report confidence intervals alongside point estimates. 4. For time series, verify stationarity (ADF/KPSS) before fitting ARIMA. 5. Use information criteria (AIC/BIC) for model selection. 6. Use robust standard errors (model.get_robustcov_results()) when heteroscedasticity is present. 7. NEVER fabricate statistical results. Every number must come from actual computation on real data.