Analysis

Statistical analysis modules for comprehensive data insights.

Univariate Analysis

Single variable descriptive statistics and distribution analysis.

Univariate statistical analysis.

Performs descriptive statistics for single variables including: - Numeric: mean, median, std, robust statistics, distribution tests - Categorical: frequencies, mode, diversity measures - Missing: missingness analysis

class statqa.analysis.univariate.UnivariateAnalyzer(handle_outliers=True, robust=True)

Bases: object

Analyzer for single-variable statistics.

Parameters:

• handle_outliers (bool)

• robust (bool)

analyze(data, variable)

Analyze a single variable.

Parameters:

• data (Series) – Data series

• variable (Variable) – Variable metadata

Returns:

Analysis results as UnivariateResult

Return type:

dict[str, Any]

batch_analyze(df, variables)

Analyze multiple variables at once.

Parameters:

• df (DataFrame) – DataFrame with data

• variables (dict[str, Variable]) – Mapping of column names to Variable metadata

Returns:

List of analysis results as dictionaries

Return type:

list[dict[str, Any]]

Bivariate Analysis

Two-variable relationship analysis including correlations and group comparisons.

Bivariate statistical analysis.

Analyzes relationships between pairs of variables: - Numeric x Numeric: Pearson/Spearman correlation, regression - Categorical x Categorical: Chi-square, Cramér’s V - Categorical x Numeric: Group comparisons, ANOVA

class statqa.analysis.bivariate.BivariateAnalyzer(significance_level=0.05, min_sample_size=10, use_robust=True)

Bases: object

Analyzer for two-variable relationships.

Parameters:

• significance_level (float)

• min_sample_size (int)

• use_robust (bool)

analyze(data, var1, var2)

Analyze relationship between two variables.

Parameters:

• data (DataFrame) – DataFrame containing both variables

• var1 (Variable) – First variable metadata

• var2 (Variable) – Second variable metadata

Returns:

Analysis results, or None if analysis not applicable

Return type:

dict[str, Any] | None

batch_analyze(df, variables, max_pairs=None)

Analyze multiple variable pairs.

Parameters:

• df (DataFrame) – DataFrame with data

• variables (dict[str, Variable]) – Mapping of variable names to metadata

• max_pairs (int | None) – Maximum number of pairs to analyze (None for all)

Returns:

List of analysis results as dictionaries

Return type:

list[dict[str, Any]]

Temporal Analysis

Time series analysis with trend detection and change point analysis.

Temporal analysis for time series data.

Analyzes trends and patterns over time: - Trend detection (Mann-Kendall, linear regression) - Seasonal decomposition - Change point detection - Year-over-year changes

class statqa.analysis.temporal.TemporalAnalyzer(significance_level=0.05, min_periods=3)

Bases: object

Analyzer for temporal patterns and trends.

Parameters:

• significance_level (float)

• min_periods (int)

analyze_trend(data, time_var, value_var)

Analyze trend in a variable over time.

Parameters:

• data (DataFrame) – DataFrame with time and value columns

• time_var (Variable) – Time variable metadata

• value_var (Variable) – Value variable being analyzed over time

Returns:

Trend analysis results as dictionary

Return type:

dict[str, Any]

analyze_grouped_trend(data, time_var, value_var, group_var)

Analyze trends separately for different groups.

Parameters:

• data (DataFrame) – DataFrame with time, value, and group columns

• time_var (Variable) – Time variable

• value_var (Variable) – Value variable

• group_var (Variable) – Grouping variable

Returns:

Grouped trend analysis results as dictionary

Return type:

dict[str, Any]

detect_change_points(data, time_var, value_var)

Detect significant change points in time series.

Uses simple segmentation approach comparing before/after means.

Parameters:

• data (DataFrame) – DataFrame with time and value columns

• time_var (Variable) – Time variable

• value_var (Variable) – Value variable

Returns:

Change point detection results as dictionary

Return type:

dict[str, Any]

year_over_year_change(data, year_var, value_var)

Calculate year-over-year changes.

Parameters:

• data (DataFrame) – DataFrame with year and value columns

• year_var (Variable) – Year variable

• value_var (Variable) – Value variable

Returns:

Year-over-year analysis results as dictionary

Return type:

dict[str, Any]

Causal Analysis

Causal inference with confounding control and sensitivity analysis.

Causal analysis with confounding control.

Performs regression analysis with control variables to surface associations in causal language: - Linear regression with controls - Logistic regression for binary outcomes - Confounder identification - Sensitivity analysis

class statqa.analysis.causal.CausalAnalyzer(significance_level=0.05, min_sample_size=30, robust_se=True)

Bases: object

Analyzer for causal relationships with confounding control.

Note: These are observational analyses and do not establish true causation without strong assumptions. Results should be interpreted as associations controlling for measured confounders.

Parameters:

• significance_level (float)

• min_sample_size (int)

• robust_se (bool)

analyze_treatment_effect(data, treatment_var, outcome_var, control_vars=None)

Estimate treatment effect on outcome with optional controls.

Parameters:

• data (DataFrame) – DataFrame with variables

• treatment_var (Variable) – Treatment/exposure variable

• outcome_var (Variable) – Outcome variable

• control_vars (list[Variable] | None) – List of control/confounder variables

Returns:

Treatment effect analysis results as dictionary

Return type:

dict[str, Any]

identify_confounders(data, treatment_var, outcome_var, potential_confounders)

Identify which variables act as confounders.

A confounder must: 1. Be associated with treatment 2. Be associated with outcome 3. Not be on causal path between treatment and outcome

Parameters:

• data (DataFrame) – DataFrame with variables

• treatment_var (Variable) – Treatment variable

• outcome_var (Variable) – Outcome variable

• potential_confounders (list[Variable]) – List of potential confounders to test

Returns:

Confounder identification results as dictionary

Return type:

dict[str, Any]