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SASInstitute A00-910

A00-910

Exam Code: A00-910

Exam Name: Statistical Thinking for Industrial Problem Solving

Updated: Jul 24, 2026

Q&A Number: 0 Q&As

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A00-910 Online Test Engine

SASInstitute A00-910 Exam Syllabus Topics:

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

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