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Statistics & Forecasting Calculators

Good forecasts and sound conclusions depend on the right statistical method. These calculators cover the full workflow: describing data, finding relationships, forecasting time series, testing hypotheses, sizing samples and monitoring processes over time.

Results are verified against standard statistical software, and each page explains how to interpret the output, not just how it is calculated.

The 13 calculators

Descriptive Statistics Calculator

Paste a data series to get central tendency, dispersion, quartiles and distribution-shape statistics, matching Excel’s sample formulas.

CAGR & Growth Rate Calculator

Measure compound annual growth between two values, or paste a series to see year-over-year changes alongside the compound rate.

Moving Average & Exponential Smoothing Forecast

Forecast a time series with simple or weighted moving averages, simple exponential smoothing, Holt’s trend method or Holt-Winters seasonal smoothing, and compare in-sample accuracy.

Linear Regression & Trend Forecast

Fit an ordinary least squares line to paired data, test the significance of the relationship, and forecast new values with confidence and prediction intervals.

Correlation Matrix Calculator

Measure the strength and direction of linear (Pearson) or rank (Spearman) relationships between two or more series, with significance tests for every pair.

Forecast Accuracy Calculator

Measure how accurate and how biased a forecast has been: percentage errors, absolute and squared errors, bias and the tracking signal used to detect a drifting forecast.

Seasonality Index Calculator

Calculate seasonal indices with the classical ratio-to-moving-average method and remove seasonality from a monthly or quarterly series to reveal the underlying trend.

Confidence Interval Calculator

Calculate a confidence interval for a population mean (t or z) or a proportion (Wald and Wilson), with the margin of error and the sample size needed for a target precision.

Hypothesis Test Calculator (t, z, Chi-Square)

Run the most common significance tests from summary statistics: one-sample and two-sample (Welch) t-tests, one- and two-proportion z-tests, and the chi-square test of independence.

Sample Size Calculator

Calculate the sample size needed to estimate a proportion or mean within a margin of error, or to detect a difference between two proportions or two means with a chosen statistical power.

Monte Carlo Profit Simulator

Simulate profit thousands of times with uncertain volume, price, unit cost and fixed costs to see the full distribution of outcomes, percentiles and the probability of missing a target.

Statistical Process Control (SPC) Chart Calculator

Calculate control limits for an X̄-R chart from subgroups or an individuals and moving range (I-MR) chart from single observations, and flag points beyond the limits and runs on one side of the center line.

Outlier Detector (Z-Score, IQR, Modified Z)

Identify unusual values in a data series using three methods side by side: the standard Z-score, Tukey’s interquartile range fences and the robust modified Z-score based on the median absolute deviation.

Choosing the right calculator

  • Understanding a data set: start with Descriptive Statistics and the Outlier Detector, then measure relationships with the Correlation Matrix.
  • Forecasting: use Moving Average & Exponential Smoothing for time series, Linear Regression for driver-based trends, Seasonality Index for monthly patterns, and Forecast Accuracy to compare methods.
  • Drawing conclusions from samples: use the Hypothesis Test, Confidence Interval and Sample Size calculators.
  • Managing risk and quality: simulate outcomes with the Monte Carlo Profit Simulator and monitor processes with SPC control charts.

Frequently asked questions

Which forecasting method is most accurate?

No single method wins everywhere. Exponential smoothing works well for short-term forecasts of stable or seasonal series; regression works when a measurable driver explains the outcome. Compare methods on held-out periods with the Forecast Accuracy Calculator.

What is the difference between correlation and regression?

Correlation measures the strength and direction of a relationship between two variables. Regression estimates the equation of that relationship so one variable can be predicted from the other, with confidence and prediction intervals.

How large should my sample be?

It depends on the precision required and the variability of the data. For a survey of a large population, about 385 responses give a margin of error of ±5 percentage points at 95% confidence. The Sample Size Calculator handles means, proportions and comparisons.

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