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SASInstitute A00-910 guide torrent - Statistical Thinking for Industrial Problem Solving

Updated: Aug 09, 2026

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A00-910 guide torrent
  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving

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SASInstitute A00-910 Exam Syllabus Topics:

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

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