Data Analysis · Interactive Case Studies
Statistics make decisions safer
Discover how statistical analyses can move your business forward — with real examples you can explore yourself.
Descriptive Statistics
Key metrics & distributions — the first look at your data
Use case: An online shop analyses its daily orders over the last quarter. Mean, variance and skewness help steer inventory, staffing and ad campaigns with data.
⚙ Adjust parameters
80
18
60
Frequency
Mean
±1 Std. dev.
Inferential Statistics — Two-sample t-test (Welch)
From sample to validated statement about the population
Use case: A food manufacturer tests two production lines. Line A (control) vs. Line B (new recipe). Is the difference in package weight statistically significant — or just chance?
⚙ Configure groups
502
510
8
30
Line A
Line B
Exploratory Data Analysis — K-Means Clustering
Automatically identify hidden customer segments
Use case: A fitness studio segments its members by visit frequency and monthly spending — without any prior knowledge. Clusters emerge automatically and enable targeted offers for each segment.
⚙ Configure clustering
3
90
Causal Analysis — Linear Regression (OLS)
Measure cause-and-effect relationships and validate them statistically
Use case: A plant manager asks: Do more training hours actually reduce the error rate? OLS regression quantifies the effect — and uses t-test and R² to check whether the relationship is statistically robust.
⚙ Simulate relationship
-1.2
2.5
45
Observations
Regression line
95% confidence band
Predictive Analysis — Time Series Forecast (Holt-Winters)
Detect historical patterns and plan the future with data
Use case: An energy provider forecasts monthly electricity consumption for the coming months — the Holt-Winters model accounts for trend, seasonality and provides prediction intervals for planning certainty.
⚙ Configure model
1.5
12
6
6
Historical
Forecast
95% prediction interval