Study mood, stress, symptom, or wellness data quickly. Compute key statistics, percentiles, and distribution checks. Built for clearer screening summaries and reflective reporting workflows.
| Participant | Weekly Wellbeing Score |
|---|---|
| P1 | 12 |
| P2 | 15 |
| P3 | 11 |
| P4 | 18 |
| P5 | 16 |
| P6 | 14 |
| P7 | 19 |
| P8 | 13 |
Univariate analysis studies one variable at a time. In mental health work, that variable may be a mood score, stress rating, symptom checklist total, sleep quality index, or burnout scale result. A clear one-variable summary helps you see the center, spread, shape, and unusual values before you compare groups or run larger models.
This page is useful for reflective practice, screening reviews, wellness program summaries, survey cleaning, and research preparation. It turns raw values into readable statistics. You can inspect mean, median, mode, quartiles, confidence intervals, percentiles, skewness, kurtosis, and IQR outliers in one place.
The mean equals the sum of all values divided by the count. Sample variance equals the squared deviations from the mean divided by n - 1. Population variance uses n. Standard deviation is the square root of variance. Standard error equals standard deviation divided by the square root of the count.
Quartiles and custom percentiles are estimated with linear interpolation on sorted values. The interquartile range equals Q3 - Q1. IQR outliers fall below Q1 - 1.5 × IQR or above Q3 + 1.5 × IQR. The confidence interval for the mean equals mean ± z × standard error.
Skewness helps describe asymmetry. Excess kurtosis helps describe tail weight. A trimmed mean reduces the effect of extreme scores by removing the selected share from each tail. Median absolute deviation offers a robust view of spread when you want less sensitivity to outliers.
Enter a variable name and unit label first. Paste one column of numeric observations into the data box. Choose the delimiter that matches your pasted values, or leave auto detect selected. Then choose sample or population settings, confidence level, decimal places, and any custom percentile or trim options.
Press Analyze Data. The result section will appear below the header and above the form. Review the summary table, interpretation notes, cleaned data table, histogram, and box plot. Then export the output as CSV or PDF for case review, reporting, teaching, or documentation.
This tool supports structured summaries. It does not diagnose any condition. Use it to understand data patterns, not to replace clinical judgment, supervision, or formal assessment standards.
It is the study of one variable only. It summarizes central tendency, spread, distribution shape, and unusual values within a single set of scores.
It helps organize score patterns from surveys, screening tools, and wellness logs. That makes reports clearer and supports better review before deeper analysis.
Choose sample when your data represents only part of a wider group. Choose population when the values represent the full group you want to describe.
Skewness shows whether scores lean left or right. Positive values suggest a longer right tail. Negative values suggest a longer left tail.
The mean uses every value and responds strongly to extremes. The median is more robust, so it is often helpful when outliers are present.
They are values far from the middle half of the dataset. The calculator flags observations outside the standard quartile-based fences.
Yes. The page includes CSV and PDF download buttons after a successful calculation. They export the visible result content for sharing or record keeping.
No. It is a statistical helper for one-variable data. Interpretation should remain grounded in proper clinical context, validated instruments, and professional judgment.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.