Statistics in controlling

Introduction to statistics in controlling

• Why are statistics important in controlling?
• Areas of application of statistics in controlling.
• Scale levels and data types in controlling data.
• Collection methods for controlling data.

Mean value ratios in controlling

• Example: Calculation of the modal value/mode for sales data.
• Example: Determining the median for salary data.
• Arithmetic, geometric and harmonic mean in budgeting.

Position distribution of data in controlling

• Quantiles and percentiles for analyzing sales distributions.
• Create and interpret histograms for production quantities.
• Use boxplots to visualize cost structures.
• Identification of outliers in financial data.

Dispersion ratios in controlling

• Linear dispersion in sales forecasts.
• Standard deviation and variance of budget deviations.
• Coefficient of variation for the comparability of key figures.

Measures of correlation in controlling

• Creation of cross tables for product market analyses.
• Bivariate analysis with pivot tables to analyze cost structures.
• Calculation of contingency coefficients for product portfolio analyses.
• Application of rank correlations in customer analysis.
• Correlation analysis to identify correlations between costs and sales.

Regression and machine learning in controlling

• Creation of regression models for sales forecasts.
• Data preparation for the analysis of cost structures.
• Implementation and interpretation of the regression analysis in the area of price elasticity.
• Splitting data into training and test data for model validation.
• Improvement of forecast models by adjusting variables.

Combinatorics in controlling

• Calculation of constellations for product variants.
• Application of random variables and probability distributions in risk analysis.

Discrete probability distributions in controlling

• Example: Calculation of the binomial distribution for the default probability of products.
• Application of the Poisson distribution to forecast maintenance requirements.
• Hypergeometric distribution in sampling for quality control.

Continuous probability distributions in controlling

• Normal distribution for analyzing financial market returns.
• Calculation of probabilities for sales forecasts.
• Use of the exponential distribution for modeling maintenance times.

Time series analysis in controlling

• Trend models for analyzing sales developments.
• Application of exponential smoothing for sales forecasts.
• Forecast models for cash flow developments.
• Evaluation of model accuracy in the sales forecast.

Confidence intervals in controlling

• Calculation of confidence intervals for sales forecasts and budgeting.

One-sided parameter tests in controlling

• Statistical tests for the average of production times.
• Test of the share value for market shares.
• Variance tests for analyzing cost variances.

Two-sided parameter tests in controlling

• Statistical tests for the average of sales prices.
• Test of the proportional value for product qualities.
• Variance tests for analyzing sales volumes.