SASInstitute A00-910 : Statistical Thinking for Industrial Problem Solving

A00-910 pass collection

Exam Code: A00-910

Exam Name: Statistical Thinking for Industrial Problem Solving

Updated: Sep 23, 2026

Q & A: 0 Questions and Answers

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SASInstitute A00-910 Exam Syllabus Topics:

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

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