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SASInstitute A00-910 Braindumps - in .pdf Free Demo

  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving
  • Last Updated: Oct 22, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving
  • Last Updated: Oct 22, 2026
  • Q & A: 0 Questions and Answers
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SASInstitute A00-910 Exam Syllabus Topics:

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

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