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SASInstitute A00-910 - Statistical Thinking for Industrial Problem Solving

Updated: Sep 01, 2026

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A00-910 Braindumps VCE
  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving

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SASInstitute A00-910 Exam Syllabus Topics:

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

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