statistics

name	statistics
description	Statistical analysis methods, hypothesis testing, and probability for data analytics
version	2.0.0
sasmp_version	2.0.0
bonded_agent	03-statistical-analysis-expert
bond_type	PRIMARY_BOND
config	{"atomic":true,"retry_enabled":true,"max_retries":3,"backoff_strategy":"exponential","numerical_precision":"high"}
parameters	{"skill_level":{"type":"string","required":true,"enum":["beginner","intermediate","advanced"],"default":"beginner"},"focus_area":{"type":"string","required":false,"enum":["descriptive","inferential","probability","regression","experiments","all"],"default":"all"},"tool_preference":{"type":"string","required":false,"enum":["python","r","excel","all"],"default":"python"}}
observability	{"logging_level":"info","metrics":["calculation_accuracy","test_validity","model_fit"]}

Statistics Skill

Overview

Master statistical concepts and methods essential for data analysis, from descriptive statistics to advanced inferential techniques.

Core Topics

Descriptive Statistics

• Measures of central tendency (mean, median, mode)
• Measures of dispersion (variance, standard deviation, IQR)
• Data distributions and skewness
• Percentiles and quartiles

Inferential Statistics

• Sampling methods and sample size determination
• Confidence intervals
• Hypothesis testing (t-tests, chi-square, ANOVA)
• P-values and statistical significance

Probability

• Basic probability rules
• Probability distributions (normal, binomial, Poisson)
• Bayes' theorem
• Expected value and variance

Regression Analysis

• Linear regression
• Multiple regression
• Logistic regression
• Model validation and diagnostics

Learning Objectives

• Apply descriptive statistics to summarize data
• Conduct hypothesis tests for business decisions
• Build and interpret regression models
• Communicate statistical findings effectively

Error Handling

Error Type	Cause	Recovery
Sample too small	Insufficient data	Increase sample or use bootstrap
Assumption violated	Data doesn't fit test	Use non-parametric alternative
Multicollinearity	Correlated predictors	Remove or combine variables
Outliers	Extreme values	Investigate or use robust methods
P-hacking	Multiple testing	Apply Bonferroni correction

Related Skills

• programming (for implementing statistical models)
• visualization (for presenting statistical insights)
• advanced (for machine learning)