SoftGuide > Functions / Modules Designation > Bootstrap method

Bootstrap method

What is meant by Bootstrap method?

The bootstrap method is a statistical technique for estimating uncertainties in estimates of statistical parameters. It relies on repeatedly drawing samples from a given dataset with replacement to create an empirical distribution of estimates. This method is particularly useful when there is no analytical solution available for uncertainties or when the assumptions for classical methods are not met.

Typical functions of software in the field of the bootstrap method could include:

  1. Generating bootstrap samples from given data.
  2. Computing statistical estimates (e.g., mean, median, quantile) based on the bootstrap samples.
  3. Constructing confidence intervals for estimates.
  4. Visualizing the bootstrap distribution or uncertainties in the estimates.

The function / module Bootstrap method belongs to:

Statistics/Forecast

analyses of covariance
Analysis
Analysis functions
Analysis results
Article hit and rivet lists
Bayesian analysis
Before-and-after comparisons
big data
Bootstrap method
Budget information
Business Impact Analysis
Calendar functions
Capacity evaluations
Cashier hit list
classification and regression trees
Cluster analyses
Clustering
Collaborative Planning
combinatorial problems
comparative statistics
Competitive Analysis
Conjoint analyses
Container accounting
Correlation analyses
Correlation matrix
Correlations
Course participant and learning statistics
Course statistics
Customer and sales data analysis
Customer evaluations
Customer statistics
Demand forecasting
Downtime Pareto analysis
Econometric and statistical analyses
Employee capacities
Energy price analysis
Error analysis
Excel export
Extrapolation procedure
Financial market statistics
Fluctuation statistics
forecast result
Forecasting
Forecasting
Forecasting and planning
Frequency distributions
Geostatistics
Gibbs sampling
Key figure simulations
KTL evaluation
Linked data management
liquidity analysis
Management evaluations
Mandate analysis
matrix calculus
Mean values
Metropolis algorithm
Movement profiles
Network Statistics
Order tracking
Performance analysis
Permutation test
Plausibility checks
Predictions
Predictive analytics
Predictive Modeling
previous year view
Probabilities of occurrence
Probability analysis
Probability distributions
Process visualization
Processing times
Project forecasts
Project ID
Projection comparison
Random generator
Real-time evaluations
Regression analysis
Regressions or equalization calculations
Resume analysis
Risk analysis
Sales comparisons
Sales hit lists
Sales lists
Sales statistics
Sales statistics
Sampling system
Scrap analyses
Seller leaderboard
Sequence analysis
Shopping cart analysis
Signal statistics
Six Sigma
statistical cost planning
Statistics
Sub-process analysis
Subscription shelf life statistics
Survival analysis
Time series analyses
Trend analyses
Trend value analyses
Utilization analysis according to loss classes
Visitor statistics
Weighting functions
What-if analyses

Software solutions with function or module Bootstrap method:

Detact®
Production Data Acquisition