SASInstitute A00-910 real exam prep : Statistical Thinking for Industrial Problem Solving

  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving
  • Updated: Aug 18, 2026
  • Q&As: 0 Questions and Answers

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SASInstitute A00-910 Exam Syllabus Topics:

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

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