統計計算機

ステップバイステップで統計を計算：平均、中央値、最頻値、分散、標準偏差、IQRを瞬時に算出。

統計計算機とは？

Statistics is the mathematical science of collecting, analyzing, interpreting, and presenting data. Whether you are a student working on a homework assignment, a researcher analyzing experimental results, or a data scientist exploring a dataset, understanding descriptive statistics is the essential first step.

This statistics calculator computes all fundamental descriptive statistics in a single calculation: measures of central tendency (mean, median, mode), measures of spread (range, variance, standard deviation, IQR), and position statistics (Q1, Q3). Every result is accompanied by a detailed step-by-step breakdown so you understand exactly how each value is derived.

Statistical literacy is increasingly vital in today's data-driven world. Medical researchers use standard deviation to quantify variability in clinical trial results. Engineers use it to monitor manufacturing quality. Educators use mean and median to summarize student performance.

計算式

Mean: x̄ = (Σxᵢ) / n

Median: middle value of sorted data (or average of two middle values for even n)

Variance (population): σ² = Σ(xᵢ − x̄)² / n

Variance (sample, Bessel-corrected): s² = Σ(xᵢ − x̄)² / (n − 1)

Standard Deviation: σ = √σ²

IQR = Q3 − Q1 (Q1 = 25th percentile, Q3 = 75th percentile)

計算方法

Enter your dataset as comma-separated, space-separated, or one-per-line numbers.
The calculator sorts the data automatically for median and quartile calculations.
Mean is computed by summing all values and dividing by count.
Median is the exact middle value (or average of two middles for even-length datasets).
Mode is the most frequent value; multiple modes are listed if they tie.
Standard deviation measures how spread out values are from the mean.
Review the step-by-step breakdown to understand each calculation in detail.

例

Dataset: 4, 8, 15, 16, 23, 42. Sorted: 4, 8, 15, 16, 23, 42. Mean = (4+8+15+16+23+42)/6 = 108/6 = 18. Median = (15+16)/2 = 15.5. No mode (all unique). Range = 42−4 = 38. σ ≈ 12.49 (population), s ≈ 13.68 (sample). Q1 = 8, Q3 = 23, IQR = 15.

主な利点

Computes 12+ statistics in one click with no manual calculation
Step-by-step breakdown shows exactly how each value is calculated
Distinguishes between population and sample standard deviation
Identifies outliers using the IQR method
Exports results to PDF and CSV for reports and further analysis

重要な用語の説明

Mean: The arithmetic average — sum of all values divided by the count
Median: The middle value that splits sorted data into two equal halves
Mode: The value that appears most frequently in the dataset
Standard deviation: The average distance of each data point from the mean
Variance: The square of standard deviation — average squared deviation from mean
IQR: Interquartile range — difference between Q3 and Q1 percentiles

この計算機を使用するタイミング

Before building any statistical model — always start with descriptive statistics
When comparing two datasets to understand which is more variable
When your teacher asks for summary statistics of a dataset
When preparing data visualizations and need to set axis scales appropriately

よくある使用例

Analyzing test scores or grades across a class
Quality control in manufacturing — detecting process variability
Clinical trials — measuring effectiveness and variability of treatments
Financial analysis — computing portfolio return statistics
Sports analytics — summarizing athlete performance metrics
Survey data analysis and social science research

よくある質問

What is the difference between population and sample standard deviation?

Population standard deviation (σ) divides by n and is used when you have data for the entire population. Sample standard deviation (s) divides by n−1 (Bessel correction) and is used when your data is a sample from a larger population, providing an unbiased estimate.

What does IQR mean in statistics?

IQR (Interquartile Range) is the difference between the 75th percentile (Q3) and the 25th percentile (Q1). It measures the spread of the middle 50% of data, making it resistant to outliers. Values more than 1.5×IQR below Q1 or above Q3 are considered outliers.

When can a dataset have no mode?

A dataset has no mode when every value appears exactly once. A dataset can also be bimodal (two modes) or multimodal (several modes) when multiple values share the highest frequency.

Why is the median sometimes preferred over the mean?

The median is preferred when data is skewed or contains extreme outliers. For example, median household income is reported instead of mean because a few billionaires would distort the mean significantly.

統計計算機