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SASInstitute A00-910

A00-910

Exam Code: A00-910

Exam Name: Statistical Thinking for Industrial Problem Solving

Updated: Aug 21, 2026

Q&A Number: 0 Q&As

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SASInstitute A00-910 Exam Syllabus Topics:

SectionWeightObjectives
Decision Making with Data15%- Perform statistical tests
  • 1. Hypothesis testing
  • 2. P-values and statistical significance
- Determine sample size
  • 1. Power analysis
  • 2. Sample size planning
- Define and describe statistical intervals
  • 1. Confidence intervals
  • 2. Prediction intervals
Statistical Thinking and Problem Solving10%- Define statistical thinking and problem solving
  • 1. Benefits and principles of statistical thinking
  • 2. Problem definition and structured problem solving
  • 3. Key Performance Indicators (KPIs) and baselines
- Process mapping and SIPOC
  • 1. Process maps
  • 2. SIPOC models
- Root cause identification and data collection
  • 1. Sampling strategies and data sources
  • 2. Cause-and-effect diagrams
  • 3. Data collection planning
  • 4. Brainstorming and affinity diagrams
  • 5. Operational definitions
Quality Methods20%- Use Statistical Process Control Charts
  • 1. Control charts
  • 2. Process monitoring
- Conduct Measurement System Studies
  • 1. Measurement system analysis
  • 2. Gage studies
- Assess process capability
  • 1. Capability analysis
  • 2. Variation assessment
Design of Experiments10%- Best practices for experiments
  • 1. Randomization
  • 2. Blocking
  • 3. Replication
- Design a simple experiment
  • 1. Factor and response selection
- Define DOE and compare it to OFAT
  • 1. Experimental design fundamentals
- Analyze experimental results
  • 1. Interpret experiment outcomes
Correlation and Regression20%- Define and use correlation
  • 1. Scatterplot interpretation
  • 2. Correlation coefficients
- Logistic regression
  • 1. Classification modeling
- Simple linear regression
  • 1. Model fitting and interpretation
- Multiple linear regression
  • 1. Multiple predictor models
Predictive Modeling and Text Mining5%- Predictive modeling concepts
  • 1. Explanatory vs predictive modeling
  • 2. Model validation
- Text mining fundamentals
  • 1. Extracting insights from text data
Exploratory Data Analysis20%- Visualize and explore data
  • 1. Bar charts
  • 2. Geographic maps
  • 3. Scatterplots
  • 4. Variability charts
  • 5. Data storytelling and communication
  • 6. Trellis plots
  • 7. Heat maps
- Prepare data for analysis
  • 1. Handling missing data and outliers
  • 2. Data quality assessment
  • 3. Grouping and binning data
  • 4. Derived variables and recoding
- Save and share results
  • 1. Create and customize graphics
  • 2. Reproducibility and work organization
- Describe data using descriptive statistics and graphical summaries
  • 1. Probability distributions and normality
  • 2. Population vs sample
  • 3. Box plots
  • 4. Summary statistics
  • 5. Run charts
  • 6. Histograms
  • 7. Pareto plots

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