Calculators

Analyze covariance to test differences between groups while controlling for continuous variables.

Create stacked and regular area charts to visualize cumulative values and trends over time.

Generate customizable bar charts to compare categories or display grouped data visually.

Calculate simple probabilities, joint probabilities, and conditional probabilities with step-by-step solutions.

Calculate probabilities and parameters for the beta distribution, useful for proportions and probabilities.

Calculate probabilities for binary outcome experiments using the binomial probability distribution.

Display data distribution characteristics and identify outliers using box-and-whisker plots.

Create multi-dimensional visualizations using bubble size to represent a third variable.

Calculate probabilities and critical values for the chi-square distribution used in various statistical tests.

Test if observed frequencies match expected frequencies in categorical data.

Test relationships between categorical variables using the chi-square test of independence.

Calculate relative variability using the coefficient of variation (CV) to compare dispersion across different datasets.

Calculate confidence intervals for Pearson correlation coefficients.

Calculate confidence intervals for the difference between two population means.

Compute confidence intervals for the difference between two population proportions.

Calculate confidence intervals for population means with known or unknown population standard deviation.

Compute confidence intervals for population proportions using sample data.

Estimate population standard deviation using confidence intervals.

Create and analyze contingency tables to examine relationships between categorical variables.

Measure the strength and direction of linear relationships between variables using Pearson's correlation coefficient.

Analyze how two variables change together by calculating their covariance and understanding their relationship.

Perform multiple pairwise comparisons after a Kruskal-Wallis test.

Compare multiple treatment groups to a single control group with type I error control.

Calculate various effect size measures to quantify the magnitude of experimental effects.

Calculate probabilities for time between events and waiting times using the exponential distribution.

Compute probabilities and critical values for the F distribution used in ANOVA and variance comparisons.

Test association between categorical variables with small sample sizes.

Generate a comprehensive five-number summary showing minimum, Q1, median, Q3, and maximum values of your dataset.

Create frequency distributions and analyze data patterns with absolute and relative frequencies.

Analyze repeated measures data with non-parametric methods.

Calculate probabilities for waiting times and right-skewed continuous data.

Compute probabilities for the number of trials until first success in Bernoulli trials.

Calculate the geometric mean for data involving rates, ratios, or exponential growth.

Compute the harmonic mean for rates and speeds, particularly useful for averaging rates or speeds.

Create color-coded matrices to visualize patterns in multivariate data or correlation matrices.

Create frequency distribution visualizations with adjustable bin sizes and interactive features.

Calculate probabilities for sampling without replacement from finite populations.

Compare three or more independent groups using non-parametric methods.

Measure the tailedness of your data distribution with kurtosis calculations and statistical interpretations.

Create interactive line charts to visualize trends and patterns in time series or sequential data.

Compare two independent groups using non-parametric methods.

Calculate central tendency measures for your data including arithmetic mean, median, and mode with detailed explanations.

Analyze relationships between multiple independent variables and a dependent variable.

Compute probabilities for the number of trials until a specified number of successes.

Calculate probabilities and critical values for the normal distribution with interactive visualizations.

Test hypotheses about a population proportion using the z-test.

Test hypotheses about a population mean with unknown population standard deviation.

Test hypotheses about a population mean with known population standard deviation.

Compare means of three or more independent groups using one-way analysis of variance.

Compare paired observations using dependent samples t-test.

Calculate percentiles, quartiles, and interquartile range (IQR) to understand data distribution and relative standing.

Model rare event probabilities using the Poisson distribution with customizable parameters.

Calculate statistical power and required sample size for various statistical tests.

Assess normality and compare data distributions using quantile-quantile plots.

Compute key measures of spread including range, variance, and standard deviation to understand data dispersion.

Analyze data from repeated measures experimental designs.

Determine required sample sizes for various statistical analyses and desired power levels.

Visualize relationships between two continuous variables with interactive scatter plots.

Model linear relationships between variables with regression analysis and prediction capabilities.

Determine the asymmetry of your data distribution by calculating skewness measures with visual interpretations.

Calculate standard errors for various statistics to assess estimation precision.

Compute probabilities and critical values for the t-distribution with adjustable degrees of freedom.

Analyze effects of three factors and their interactions on a dependent variable.

Perform pairwise comparisons after ANOVA with family-wise error rate control.

Compare two population proportions using the z-test.

Compare means of two independent groups using the t-test.

Compare two population means with known population standard deviations.

Analyze effects of two factors and their interaction on a dependent variable.

Calculate probabilities for events with equal likelihood over an interval.

Combine box plot and kernel density estimation to show full data distribution shape.

Model reliability and failure time data using the Weibull distribution.

Compare paired observations using non-parametric methods.

Convert raw scores to standardized z-scores to compare values from different distributions.