DocsStatistics

Statistics Tools

Statistical analysis tools for exploring data distributions, relationships, and significance.

Overview

Descriptive Statistics

Summary statistics and distributions

Hypothesis Testing

Statistical significance tests

Correlation Analysis

Relationship detection

Regression Analysis

Predictive modeling

Descriptive Statistics

Tool	Description	Shortcut
Summary Stats	Mean, median, std, min, max, quartiles	Ctrl+Shift+S
Distribution Plot	Histogram with KDE overlay	-
Box Plot	Quartile visualization with outliers	-
Violin Plot	Distribution shape comparison	-

Hypothesis Testing

Tool	Description	Use Case
T-Test	Compare two group means	A/B testing
ANOVA	Compare multiple group means	Multi-group comparison
Chi-Square	Test categorical independence	Feature relationships
Kolmogorov-Smirnov	Test distribution normality	Assumption checking
Mann-Whitney U	Non-parametric comparison	Non-normal data
Wilcoxon	Paired non-parametric test	Before/after analysis

Regression Analysis

Tool	Description	Use Case
Linear Regression	Single predictor	Simple relationships
Multiple Regression	Multiple predictors	Complex modeling
Logistic Regression	Binary classification	Probability estimation
Polynomial Regression	Non-linear fitting	Curved relationships

Scripting Functions

import cyxwiz.stats as stats

# Basic statistics
mean = stats.mean(data['column'])
median = stats.median(data['column'])
std = stats.std(data['column'])

# Percentiles
q1, q2, q3 = stats.quartiles(data['column'])

# T-test
result = stats.ttest_ind(group_a, group_b)
print(f"t={result.statistic}, p={result.pvalue}")

# Correlation
r, p = stats.pearsonr(x, y)
corr_matrix = stats.correlation_matrix(data)

# Linear regression
model = stats.linear_regression(X, y)
print(f"R2: {model.r_squared}")
predictions = model.predict(X_new)

Export Options

Format	Content
CSV	Raw statistics data
JSON	Structured results
HTML	Formatted report
LaTeX	Publication-ready tables
Python	Reproducible script