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SASInstitute A00-910 Exam Syllabus Topics:

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