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SASInstitute A00-910 Exam Syllabus Topics:

SectionWeightObjectives
Decision Making with Data15%- Determine sample size
  • 1. Power analysis
  • 2. Sample size planning
- Define and describe statistical intervals
  • 1. Prediction intervals
  • 2. Confidence intervals
- Perform statistical tests
  • 1. P-values and statistical significance
  • 2. Hypothesis testing
Exploratory Data Analysis20%- Prepare data for analysis
  • 1. Handling missing data and outliers
  • 2. Grouping and binning data
  • 3. Data quality assessment
  • 4. Derived variables and recoding
- Save and share results
  • 1. Create and customize graphics
  • 2. Reproducibility and work organization
- Visualize and explore data
  • 1. Variability charts
  • 2. Data storytelling and communication
  • 3. Scatterplots
  • 4. Bar charts
  • 5. Geographic maps
  • 6. Trellis plots
  • 7. Heat maps
- Describe data using descriptive statistics and graphical summaries
  • 1. Box plots
  • 2. Population vs sample
  • 3. Histograms
  • 4. Summary statistics
  • 5. Probability distributions and normality
  • 6. Pareto plots
  • 7. Run charts
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. Process maps
  • 2. SIPOC models
- 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
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
Correlation and Regression20%- Simple linear regression
  • 1. Model fitting and interpretation
- Multiple linear regression
  • 1. Multiple predictor models
- Logistic regression
  • 1. Classification modeling
- Define and use correlation
  • 1. Correlation coefficients
  • 2. Scatterplot interpretation
Design of Experiments10%- Define DOE and compare it to OFAT
  • 1. Experimental design fundamentals
- Best practices for experiments
  • 1. Replication
  • 2. Blocking
  • 3. Randomization
- Design a simple experiment
  • 1. Factor and response selection
- Analyze experimental results
  • 1. Interpret experiment outcomes
Predictive Modeling and Text Mining5%- Text mining fundamentals
  • 1. Extracting insights from text data
- Predictive modeling concepts
  • 1. Model validation
  • 2. Explanatory vs predictive modeling

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