Hypothesis Testing and Regression for Process Improvement

Programme Summary

Use statistical inference to distinguish meaningful process differences from ordinary variation. Participants select tests, interpret confidence intervals and build regression models around practical improvement questions. Data exercises emphasise assumptions, sampling, effect size and residual checks, enabling professionals to explain the strength and limits of their conclusions rather than relying on a significance value alone.

Training Objectives

1 Connect statistical methods with operational questions; 2 Select tests appropriate to data and design; 3 Interpret uncertainty and practical significance responsibly; 4 Build and diagnose useful regression models; 5 Communicate evidence and analytical limitations clearly.

Learning Outcomes

1 Formulate testable process comparison hypotheses; 2 Select and interpret an appropriate test; 3 Explain confidence intervals and effect sizes; 4 Build and assess a regression model; 5 Present a justified statistical improvement conclusion.

Target Audience

Quality Engineers; Process Analysts; Improvement Specialists; Operations Analysts; Manufacturing Engineers.

Target Industries

Automotive Manufacturing; Electronics Manufacturing; Industrial Equipment Manufacturing; Medical Device Manufacturing; Consumer Goods Manufacturing; Industrial Services.

Course Contents

• Module 1 Questions and Data Structure: Comparison association and prediction questions; Independent paired and grouped observations; Operational definitions and sampling limitations.
• Module 2 Probability and Sampling Variation: Population parameters and sample estimates; Sampling distributions and standard errors; Random variation and uncertainty interpretation.
• Module 3 Hypotheses and Decision Errors: Null and alternative hypotheses; Type one and type two errors; Significance thresholds and decision consequences.
• Module 4 Confidence Intervals and Effect Size: Interval estimates for operational parameters; Practical versus statistical significance; Precision and sample size considerations.
• Module 5 Comparing Means: One sample and two sample tests; Paired comparisons and design assumptions; Variance assumptions and interpretation safeguards.
• Module 6 Comparing Proportions and Variation: Proportion tests and suitable sample conditions; Variance comparisons and distribution sensitivity; Count data and test limitations.
• Module 7 Multiple Group Comparisons: Analysis of variance and group effects; Post hoc comparisons and error control; Interpreting group differences with context.
• Module 8 Nonparametric Alternatives: Rank based comparison methods; When distribution assumptions are unsuitable; Interpretation and power tradeoffs.
• Module 9 Simple Regression: Response predictor and fitted relationship; Slope intercept and prediction intervals; Correlation versus causal explanation.
• Module 10 Multiple Regression and Diagnostics: Predictor selection and collinearity; Residual plots and influential observations; Validation and extrapolation limitations.
• Module 11 Communicating Statistical Evidence: Clear charts and decision relevant summaries; Assumptions uncertainty and unresolved questions; Avoiding selective reporting and overstatement.
• Module 12 Statistical Analysis Workshop: Selecting tests for a process case; Interpreting a regression and diagnostic output; Presenting evidence based improvement recommendations.

Assessment

• 60 single best answer questions, including applied scenarios; one mark per question.
• 90 minute invigilated digital examination with identity verification. Approved accessibility adjustments preserve the assessed competence.
• Supplied reference pack and calculator permitted; external internet and AI prohibited.
• Individual applied assessment (45 minutes): Select and interpret tests for supplied process questions, evaluate regression diagnostics and write a concise conclusion including practical significance and limitations.
• Assessment criteria: Correct method and assumption selection; Accurate interpretation of statistical evidence; Clear practical conclusion and limitations.
• Pass each component separately: 45/60 written marks (75%) and at least 70% on the applied task.
• Individual results and feedback are provided. One reassessment may follow coaching under the published enrolment terms.

Certification

• Successful learners receive a TQCSI Arabia digital Certificate of Successful Completion.
• The certificate records the learner name, exact programme title, teaching hours, completion date and unique verification reference.
• Verified identity, full attendance and separate written and applied passes are required; all mandatory critical criteria must be met.
• Proposed issue target: within 30 calendar days of approved results and settlement of applicable fees.
• Records statistical inference and regression skills; analytical results require suitable data and competent review before operational use.
• Attendance confirmation may record completed hours for unsuccessful learners; it does not certify successful completion.

Programme Profile

Number of daysNumber of daysNumber of days

333

Total teaching hoursTotal teaching hoursTotal teaching hours

18 hours; breaks, pre course work and formal final assessments excluded.18 hours; breaks, pre course work and formal final assessments excluded.18 hours; breaks, pre course work and formal final assessments excluded.

Delegates allowedDelegates allowedDelegates allowed

Maximum 16 delegates.Maximum 16 delegates.Maximum 16 delegates.

Training prerequisitesTraining prerequisitesTraining prerequisites

Basic descriptive statistics and spreadsheet competence are required. Familiarity with operational data collection is recommended.Basic descriptive statistics and spreadsheet competence are required. Familiarity with operational data collection is recommended.Basic descriptive statistics and spreadsheet competence are required. Familiarity with operational data collection is recommended.

Pre course assignment and assessmentPre course assignment and assessmentPre course assignment and assessment

Complete a 30-minute diagnostic on data types, averages, variability and graphical interpretation.Complete a 30-minute diagnostic on data types, averages, variability and graphical interpretation.Complete a 30-minute diagnostic on data types, averages, variability and graphical interpretation.

End of course assessmentEnd of course assessmentEnd of course assessment

60 question written exam (90 minutes) and individual applied task (45 minutes).60 question written exam (90 minutes) and individual applied task (45 minutes).60 question written exam (90 minutes) and individual applied task (45 minutes).

Passing criteriaPassing criteriaPassing criteria

75% written and 70% applied, passed separately; full attendance.75% written and 70% applied, passed separately; full attendance.75% written and 70% applied, passed separately; full attendance.