Minitab 20 Statistical Software

Minitab is used in both industry and academia. Minitab caters to the statistical analysis and visualization needs of a wide spectrum of users - from absolute beginners to the seasoned analytics experts, and six sigma black belts.

Do I need to be a statistical expert to use Minitab?

Absolutely not! Even if you are not a statistics expert, Minitab makes it easy for you to learn statistics by helping users focus on the analysis results and the impact of the result on a business / practical scenario, rather than spending time on learning formulas and performing complicated statistical calculations.

Minitab 19’s assistant feature is designed to:

• Help beginners select the right tools,
• Guide users through each step of the analysis
• Helps interpreting and presenting the results.

Which industries use Minitab?

90% of fortune 100 companies use Minitab to analyse their data, and help improve their processes, get deep insights, design experiments, and solve some of the most difficult problems. Few of the industries using Minitab:

Data drives the businesses of today, and will continue to do so more in the future. Minitab is used by a professionals of various functions. Few of the examples are listed below:

For Quality

• Measurement System Analysis (Gage studies, Attribute agreement analysis, Capability Analysis)
• Control Charts ( Variable, attribute, Multivariate, Time weighted, Rare event charts)
• Acceptance Sampling
• Tolerance Intervals

For Reliability Engineering

• Distribution Analysis: Arbitrary censoring, Weibull analysis, Censored data
• Warranty Analysis
• Repairable Systems Analysis
• Test Plans: Demonstration, Sample Size Estimation , Accelerated Life Test
• Regression with life data
• Probit Analysis

For Product Development

• Design of Experiments (DOE): Screening designs, Full factorial, Fractional factorial, Response surface, Mixture
• Power sample size: Tolerance intervals, Normal and non-normal distributions

For Business Analytics

• Correlation
• Statistical Modeling: Linear / Non-linear Regression, Multivariate Models, Cluster Analysis, Time Series Analytics, ARIMA modeling, Time series / forecasting
• Multivariate methods
• Chi-Square Test for Association

For Process Validation

• Stage 1 Process Design: Measurement Systems Analysis, Hypothesis testing, Regression / ANOVA, Process Capability
• Stage 2 Process Qualification:  Control charts, Capability analysis, Tolerance Intervals
• Stage 3 Continued Process Validation: Measurement System Analysis, Acceptance sampling, Control charts