Quick Start Guide

This guide will get you up and running with Real Simple Stats in just a few minutes.

Basic Usage

Import and Calculate Statistics

from real_simple_stats import descriptive_statistics as desc

# Sample data
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Calculate basic statistics
mean_val = desc.mean(data)
median_val = desc.median(data)
std_dev = desc.standard_deviation(data)
variance = desc.variance(data)

print(f"Mean: {mean_val}")
print(f"Median: {median_val}")
print(f"Standard Deviation: {std_dev:.2f}")
print(f"Variance: {variance:.2f}")

Output:

Mean: 5.5
Median: 5.5
Standard Deviation: 3.03
Variance: 9.17

Probability Calculations

from real_simple_stats import probability_utils as prob
from real_simple_stats import normal_distributions as norm
from real_simple_stats import binomial_distributions as binom

# Normal distribution
pdf_value = norm.normal_pdf(x=0, mean=0, std_dev=1)
cdf_value = norm.normal_cdf(x=1.96, mean=0, std_dev=1)
print(f"Normal PDF at 0: {pdf_value:.6f}")
print(f"Normal CDF at 1.96: {cdf_value:.6f}")

# Binomial probability
prob_binom = binom.binomial_probability(n=10, k=3, p=0.5)
print(f"Binomial P(X=3): {prob_binom:.6f}")

# Combinations and permutations
n, k = 10, 3
combinations = prob.combinations(n, k)
permutations = prob.permutations(n, k)

print(f"Combinations C({n},{k}): {combinations}")
print(f"Permutations P({n},{k}): {permutations}")

Output:

Normal PDF at 0: 0.398942
Normal CDF at 1.96: 0.975002
Binomial P(X=3): 0.117188
Combinations C(10,3): 120
Permutations P(10,3): 720

Hypothesis Testing

from real_simple_stats import hypothesis_testing as ht

# Sample data for t-test
sample_data = [23, 25, 27, 24, 26, 28, 22, 29, 25, 27]

# Calculate t-score
sample_mean = 25.6
population_mean = 24.0
sample_std = 2.1
n = len(sample_data)

t_score = ht.t_score(sample_mean, population_mean, sample_std, n)
print(f"T-score: {t_score:.3f}")

# Get critical value
alpha = 0.05
df = n - 1
critical_val = ht.critical_value_t(alpha, df)
print(f"Critical value (α=0.05, df={df}): {critical_val:.3f}")

Working with Distributions

from real_simple_stats import binomial_distributions as binom

# Binomial probability
n_trials = 10
k_successes = 3
p_success = 0.5

prob_exact = binom.binomial_probability(n_trials, k_successes, p_success)
print(f"P(X = {k_successes}): {prob_exact:.4f}")

# Expected value and variance
expected = binom.binomial_expected_value(n_trials, p_success)
variance = binom.binomial_variance(n_trials, p_success)

print(f"Expected value: {expected}")
print(f"Variance: {variance}")

Command Line Interface

Real Simple Stats includes a powerful CLI for quick calculations:

Basic Statistics

# Calculate mean
rss-calc stats --data "1,2,3,4,5" --stat mean

# Calculate multiple statistics
rss-calc stats --data "10,20,30,40,50" --stat all

Probability Calculations

# Normal distribution PDF
rss-calc prob --type normal --x 0 --mean 0 --std 1

# Normal distribution CDF
rss-calc prob --type normal --x 1.96 --mean 0 --std 1 --cdf

# Binomial probability
rss-calc prob --type binomial --n 10 --k 3 --p 0.5

# Bayes' theorem
rss-calc prob --type bayes --p_b_given_a 0.9 --p_a 0.01 --p_b 0.05

Glossary Lookup

# Look up statistical terms
rss-calc glossary --term "standard deviation"
rss-calc glossary --term "p-value"

Common Workflows

Analyzing a Dataset

from real_simple_stats import descriptive_statistics as desc
import real_simple_stats.plots as plots

# Your dataset
scores = [78, 85, 92, 88, 76, 89, 94, 82, 87, 91, 79, 86]

# Comprehensive analysis
print("Dataset Analysis")
print("=" * 20)
print(f"Sample size: {len(scores)}")
print(f"Mean: {desc.mean(scores):.2f}")
print(f"Median: {desc.median(scores):.2f}")
print(f"Mode: {desc.mode(scores)}")
print(f"Range: {max(scores) - min(scores)}")
print(f"Standard deviation: {desc.standard_deviation(scores):.2f}")
print(f"Coefficient of variation: {desc.coefficient_of_variation(scores):.2f}%")

Comparing Two Groups

from real_simple_stats import descriptive_statistics as desc
from real_simple_stats import hypothesis_testing as ht

# Two groups of data
group_a = [23, 25, 27, 24, 26, 28, 22, 29]
group_b = [30, 32, 28, 31, 33, 29, 35, 30]

# Compare means
mean_a = desc.mean(group_a)
mean_b = desc.mean(group_b)

print(f"Group A mean: {mean_a:.2f}")
print(f"Group B mean: {mean_b:.2f}")
print(f"Difference: {mean_b - mean_a:.2f}")

# Calculate effect size (if available)
std_a = desc.standard_deviation(group_a)
std_b = desc.standard_deviation(group_b)

print(f"Group A std: {std_a:.2f}")
print(f"Group B std: {std_b:.2f}")

Next Steps

Next steps:

Explore the API Reference - See what functions are available
Check out Tutorials - Work through some examples
Try the CLI - Use the command-line tool for quick calculations
Read the Examples - See how others are using the library

Common Patterns

Error Handling

from real_simple_stats import descriptive_statistics as desc

try:
    result = desc.mean([])  # Empty list
except ValueError as e:
    print(f"Error: {e}")

try:
    result = desc.standard_deviation([5])  # Single value
except ValueError as e:
    print(f"Error: {e}")

Working with Different Data Types

# Real Simple Stats works with various numeric types
integers = [1, 2, 3, 4, 5]
floats = [1.5, 2.7, 3.2, 4.8, 5.1]
mixed = [1, 2.5, 3, 4.7, 5]

# All work the same way
print(f"Integer mean: {desc.mean(integers)}")
print(f"Float mean: {desc.mean(floats):.2f}")
print(f"Mixed mean: {desc.mean(mixed):.2f}")

Getting Help

Documentation: Check the API reference for function details
Examples: See the examples/ directory in the repository
Issues: Report bugs or request features on GitHub

Ready to dive deeper? Check out the Tutorials section for more examples!