Interactive Statistics Toolkit

Statistical Calculators

Descriptive Statistics Calculator

Enter your data (comma separated):

Results

Mean: -

Median: -

Mode: -

Standard Deviation: -

Variance: -

Range: -

Probability Calculator

Distribution Type:

Mean (μ):

Standard Deviation (σ):

Lower Bound:

Upper Bound:

Results

Probability: -

Cumulative Probability: -

Hypothesis Testing Calculator

Test Type:

Sample Mean:

Population Mean:

Population Std Dev:

Sample Size:

Alternative Hypothesis:

Significance Level (α):

Test Results

Test Statistic: -

p-value: -

Critical Value: -

Conclusion: -

Regression Calculator

X Values (comma separated):

Y Values (comma separated):

Regression Results

Intercept (b₀): -

Slope (b₁): -

R-squared: -

Equation: -

Python Code

# Python code for linear regression
import numpy as np
from scipy import stats

# Your data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Perform linear regression
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)

print(f"Equation: y = {intercept:.2f} + {slope:.2f}x")
print(f"R-squared: {r_value**2:.4f}")
                        

Data Playground

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Visualization

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Statistical Concepts

Descriptive Statistics

Measures of central tendency, dispersion, and shape of data distributions.

Inferential Statistics

Drawing conclusions about populations based on sample data.

Hypothesis Testing

Making decisions using statistical evidence from data.

Regression Analysis

Modeling relationships between variables.

Probability Distributions

Models for random variables and their probabilities.

Central Limit Theorem

Foundation for many statistical methods.