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Quest Training

Applied Statistics for Quality Professionals Training Course

Gives quality professionals the statistical methods to interpret quality data, understand variation and base process decisions on sound evidence.

SPC · Statistical Process Control & Quality AnalyticsAll Levels#2917ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Quality Management & Operational ExcellenceCategories:Statistical Process Control & Quality Analytics

Course Overview

Applied statistics provides quality professionals with the analytical skills needed to evaluate process performance, interpret quality data, identify variation, and make evidence-based decisions. By applying statistical methods to real operational data, organizations can improve product consistency, reduce defects, strengthen process control, and support continuous improvement initiatives. This course focuses on the practical application of statistical techniques in quality management, including descriptive statistics, probability distributions, sampling methods, hypothesis testing, correlation and regression, statistical process control (SPC), and process capability analysis. Participants will learn how to select appropriate statistical methods, interpret results accurately, recognize common analytical errors, and translate numerical findings into meaningful quality insights. Through practical exercises, case studies, and quality-related datasets, participants will develop the ability to analyze performance trends, investigate variation, evaluate improvement initiatives, and communicate statistical findings effectively. The course emphasizes practical decision-making and the integration of statistical analysis into inspection, process monitoring, corrective action, and quality improvement activities.

Learning Objectives

  • Explain fundamental statistical concepts and their applications in quality management.
  • Calculate and interpret measures of central tendency and data dispersion.
  • Apply probability concepts and statistical distributions to quality-related problems.
  • Select appropriate sampling methods for inspection and quality assessment.
  • Use confidence intervals and hypothesis testing to evaluate quality data.
  • Analyze relationships between variables using correlation and regression techniques.
  • Apply Statistical Process Control (SPC) methods to monitor process behavior.
  • Calculate and interpret process capability and performance indices.
  • Identify statistically significant variation and distinguish it from routine process fluctuations.
  • Present statistical findings in clear reports that support corrective action and continuous improvement.

Who Should Attend

Quality assurance and quality control managers and engineers. Quality inspectors, analysts, and laboratory professionals. Manufacturing, production, and process engineers. Continuous improvement and operational excellence specialists. Six Sigma practitioners and process improvement professionals. Supplier quality and incoming inspection specialists. Reliability and maintenance engineers involved in performance analysis. Internal auditors and compliance professionals working with quality data. Production supervisors and technical team leaders. Professionals responsible for quality reporting, performance measurement, and data-driven decision-making.

Learning Outcomes

  • Summarize and interpret quality datasets using descriptive statistical measures.
  • Evaluate data distribution, variability, and potential outliers.
  • Select appropriate sampling approaches based on quality objectives and data characteristics.
  • Interpret probability distributions and their relevance to process performance.
  • Formulate statistical hypotheses and interpret test results appropriately.
  • Use confidence intervals to assess uncertainty in quality measurements.
  • Evaluate relationships between process variables using correlation and regression.
  • Interpret control charts and recognize signals of process instability.
  • Apply capability indices such as Cp and Cpk when their underlying assumptions are satisfied.
  • Communicate statistical conclusions and translate them into practical quality improvement actions.

Course Outline

Day 1

Statistical Foundations for Quality Management

  • The role of applied statistics in quality assurance and continuous improvement.
  • Types of data: categorical, discrete, and continuous variables.
  • Data collection, classification, organization, and validation.
  • Measures of central tendency: mean, median, and mode.
  • Measures of dispersion: range, variance, standard deviation, and interquartile range.
  • Frequency distributions, histograms, box plots, and graphical data interpretation.
  • Practical Exercise: Analyze a quality dataset to summarize performance, variation, and potential quality concerns.
Day 2

Probability, Distributions, and Sampling

  • Fundamental probability concepts and their applications in quality assessment.
  • Normal, binomial, and Poisson distributions in quality-related scenarios.
  • Understanding sampling methods, sample size, and representativeness.
  • Sampling variation and the relationship between samples and populations.
  • Sampling distributions and the central limit theorem.
  • Confidence intervals and interpreting uncertainty in quality measurements.
  • Practical Exercise: Select an appropriate sampling method and distribution to evaluate a simulated inspection dataset.
Day 3

Statistical Inference and Relationship Analysis

  • Statistical hypotheses, null and alternative hypotheses, and significance levels.
  • Hypothesis testing and interpretation of p-values.
  • Type I and Type II errors and their implications for quality decisions.
  • Selecting appropriate statistical tests for different data types and study objectives.
  • Comparing means and proportions across groups or process conditions.
  • Correlation analysis and interpretation of relationships between variables.
  • Introduction to simple linear regression for examining process relationships.
  • Practical Exercise: Evaluate a quality improvement claim using an appropriate statistical test and interpret the findings.
Day 4

Statistical Process Control and Process Capability

  • Principles and applications of Statistical Process Control (SPC).
  • Common-cause and special-cause variation.
  • Selecting and interpreting control charts for variables and attributes.
  • Recognizing patterns, trends, and signals of process instability.
  • Process capability and performance analysis using Cp, Cpk, Pp, and Ppk.
  • Relationship between process stability, capability, specification limits, and defect risk.
  • Practical Exercise: Interpret control charts and capability results to assess process performance and recommend appropriate action.
Day 5

Applied Statistical Analysis and Quality Improvement

  • Integrating statistical analysis into problem-solving and root cause investigations.
  • Evaluating before-and-after performance using suitable analytical methods.
  • Identifying trends, unusual observations, and potential sources of variation.
  • Using statistical evidence to support corrective and preventive actions.
  • Presenting results through tables, charts, and concise quality reports.
  • Establishing practical guidelines for selecting and applying statistical methods.
  • Practical Exercise: Complete an applied quality analysis, interpret the results, and present evidence-based recommendations for process improvement.

Upcoming Dates

Selected date

Cape Town · 9 – 13 Nov 2026 · Classroom

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CityDatesPrice per ParticipantEarly Bird
AbujaConfirmed
9 – 13 Nov 2026€5,100—
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Cape TownScheduled
9 – 13 Nov 2026€5,100—
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CasablancaScheduled
9 – 13 Nov 2026€4,300—
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DammamScheduled
15 – 19 Nov 2026€4,200—
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DohaScheduled
22 – 26 Nov 2026€4,400€3,960until 2 Nov
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AccraConfirmed
23 – 27 Nov 2026€5,100€4,590until 2 Nov
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DubaiScheduled
29 Nov – 3 Dec 2026€4,400€3,960until 9 Nov
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ErbilScheduled
6 – 10 Dec 2026€3,100€2,790until 16 Nov
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IstanbulScheduled
7 – 11 Dec 2026€4,700€4,230until 16 Nov
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JeddahScheduled
13 – 17 Dec 2026€4,200€3,780until 23 Nov
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AccraScheduled
14 – 18 Dec 2026€5,100€4,590until 23 Nov
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KigaliScheduled
21 – 25 Dec 2026€5,100€4,590until 30 Nov
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LisbonScheduled
28 Dec 2026 – 1 Jan 2027€5,400€4,860until 7 Dec
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Kuala LumpurScheduled
28 Dec 2026 – 1 Jan 2027€4,400€3,960until 7 Dec
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LondonScheduled
4 – 8 Jan 2027€5,600€5,040until 14 Dec
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MadinahScheduled
11 – 15 Jan 2027€4,200€3,780until 21 Dec
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AmmanConfirmed
17 – 21 Jan 2027€3,100€2,790until 28 Dec
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MakkahScheduled
18 – 22 Jan 2027€4,200€3,780until 28 Dec
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MadridScheduled
18 – 22 Jan 2027€5,400€4,860until 28 Dec
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ManamaScheduled
25 – 29 Jan 2027€4,700€4,230until 4 Jan
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MuscatScheduled
1 – 5 Feb 2027€5,300€4,770until 11 Jan
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OnlineScheduled
7 – 11 Feb 2027€1,700€1,530until 18 Jan
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ParisScheduled
8 – 12 Feb 2027€5,700€5,130until 18 Jan
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RiyadhScheduled
15 – 19 Feb 2027€4,200€3,780until 25 Jan
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AmsterdamScheduled
15 – 19 Feb 2027€5,400€4,860until 25 Jan
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SalalahScheduled
22 – 26 Feb 2027€5,300€4,770until 1 Feb
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AbujaScheduled
1 – 5 Mar 2027€5,100€4,590until 8 Feb
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AmmanScheduled
7 – 11 Mar 2027€3,100€2,790until 15 Feb
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AmsterdamScheduled
15 – 19 Mar 2027€5,400€4,860until 22 Feb
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BakuScheduled
22 – 26 Mar 2027€4,800€4,320until 1 Mar
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