STA100 - Industrial Statistics, SQC and SPC
Apply modern statistical methods to real plant data - 6 hours online, covering statistical quality control, statistical process control, and Six Sigma, finishing with a completion certificate.
Industrial Statistics, SQC and SPC
The STA100 course trains engineers, technicians, and supervisors on the latest statistical tools, methods, and practices for analyzing process and plant data. Known formally as PiControl's Industrial Statistics, SQC and SPC course, STA100 runs entirely online in 6 hours, using training slides and various statistical software products throughout.
STA100 covers statistical quality control, statistical process control, and Six Sigma, along with the related methods that support them: descriptive statistics, control charts, capability indices, design of experiments, gauge studies, regression, and reliability analysis. Attendees learn to understand customer quality needs and implement monitoring and statistical methods that improve control, so they leave able to apply statistical principles directly to their own plant data and control problems.
What You Learn in the STA100 Statistics Course
STA100 moves from descriptive statistics to applied statistical process control, design of experiments, and reliability analysis, so attendees can analyze, interpret, and present plant data in a way that drives real decisions. After completing the course, attendees can determine when a real process problem exists, build and read the right control chart for the situation, size an experiment correctly, and quantify measurement system and process capability rather than relying on guesswork.
The course covers the following topics.
Descriptive statistics and data analysis fundamentals
- Analyzing, interpreting, and presenting data in a meaningful way
- Descriptive statistics
- Histograms, Pareto charts, and scatter plots
- Confidence intervals, T-tests, and F-tests
- Sampling strategies and transformations
- Power and sample size calculations
- Analysis of variance (ANOVA), non-parametric tests, and regression
Statistical process control and capability
- Determining when a real process problem exists and when changes are required
- X and mR, Xbar and R control charts
- c, u, np, and p attribute control charts
- CUSUM and EWMA charts
- Capability indices Cp, Cpk, Pp, and Ppk
- Time series plots, trend analysis, and decomposition
- Moving averages and other smoothing methods
- Statistical hypothesis tests and equivalence testing
- One-way analysis of variance and confidence intervals for validation
Experimental design and measurement systems
- Problem definition, and selecting responses and factors
- Scoping studies and screening designs
- Taguchi methods, and fractional and full factorial designs
- Response surface methodology (RSM)
- Product design, semantic scales, and questionnaire design
- Factor analysis and principal components analysis (PCA)
- Gauge repeatability and reproducibility (Gauge R&R) studies
- Gauge linearity and bias studies, and attribute agreement analysis
Regression, reliability, and Six Sigma applications
- Estimating relationships between independent and dependent variables
- Explaining relationships among variables to predict actual responses
- Product and system lifetime analysis
- Product reliability and failure modes
- Manufacturer's methods to inform warranty periods
- First-time and terminal failure rates
- Non-repairable devices and repairable systems
- Test plans and the Weibull distribution
STA100 is structured around real plant use cases, so the statistical tools and control charts covered here can be applied directly to your own process and quality data.
Who the Course Is For
STA100 is built for the people responsible for process and product quality data in the plant and lab: process engineers, research engineers, laboratory personnel, analyzer technicians, instrument engineers, and supervisors. No prerequisites are required, because the course is designed for individuals at all levels, from beginners to those looking to enhance their statistical knowledge.
Process & research engineers
Apply control charts, capability indices, and design of experiments to real plant data, so process improvements are driven by statistical evidence rather than intuition alone.
Lab, analyzer & instrument staff
Learn gauge repeatability and reproducibility studies, gauge linearity and bias studies, and attribute agreement analysis to validate the measurement systems quality decisions depend on.
QA/QC teams & supervisors
Gain the vocabulary and judgment to read control limits, interpret capability studies, and support ISO, FDA, and customer quality audits with defensible, data-driven process control.
Bring your whole team
STA100 also suits full teams from a single plant or lab. Group participation works well when a team is responsible for a quality system, Six Sigma initiative, or SPC rollout, and companies use the course for QA/QC onboarding and statistical upskilling. With prior approval, attendees can bring anonymized plant data for discussion during the session, and onsite corporate training is available on request.
Statistical Process Control in Practice
A control chart only helps if attendees can read it correctly under real plant conditions. STA100 teaches the X and mR, Xbar and R, c, u, np, p, CUSUM, and EWMA charts against the same question every one of them answers: has a real process problem occurred, or is the variation just normal process noise? Attendees learn to set centerlines and control limits, recognize out-of-control signals, and decide when a process change is actually required.
The same data also drives capability analysis. Once a process is shown to be in statistical control, attendees calculate Cp, Cpk, Pp, and Ppk to quantify how well it meets specification, and use time series analysis, trend analysis, and smoothing methods to separate real drift from short-term variation. These are the core SQC and SPC skills behind Six Sigma process improvement work.
Certification and Course Materials
STA100 attendees receive a PiControl Solutions completion certificate, which can be added to professional profiles or submitted for PDH or CEU credits, subject to local approval. The course is structured around real plant use cases and provides practical templates, workflows, and examples that can be implemented right after training, rather than relying on a written exam alone to confirm understanding.
Attendees also receive training slides and access to various statistical software products used during the course. With prior approval, attendees can bring anonymized plant data or a case study for discussion or troubleshooting during the session. Engineers who want to continue into applied process control follow STA100 with PID100 or, for time-domain identification methods, STA200.
- Completion certificate
- PDH & CEU eligible
- Digital training slides
- Statistical software access
- Practical templates & workflows
- STA200 & PID100 follow-on pathway
STA100 Frequently Asked Questions
Short answers to the questions engineers ask most before enrolling in the STA100 statistics course.
Get Started With STA100
Request course info for STA100 to give your engineering, quality, and lab teams a practical, plant-data-driven statistics course with a completion certificate. The course runs entirely online in 6 hours, so teams in any location or time zone can start.
Related course: STA200 - System Identification & Statistical Analysis. Questions: [email protected], Tel: (832) 495 6436.