SASInstitute Statistical Thinking for Industrial Problem Solving - testinsides A00-910 dumps

A00-910 real exams

Exam Code: A00-910

Exam Name: Statistical Thinking for Industrial Problem Solving

Updated: Sep 08, 2026

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SASInstitute A00-910 Exam Syllabus Topics:

SectionWeightObjectives
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
Quality Methods20%- Use Statistical Process Control Charts
  • 1. Control charts
  • 2. Process monitoring
- Conduct Measurement System Studies
  • 1. Measurement system analysis
  • 2. Gage studies
- Assess process capability
  • 1. Variation assessment
  • 2. Capability analysis
Statistical Thinking and Problem Solving10%- 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. SIPOC models
  • 2. Process maps
- Root cause identification and data collection
  • 1. Cause-and-effect diagrams
  • 2. Sampling strategies and data sources
  • 3. Data collection planning
  • 4. Brainstorming and affinity diagrams
  • 5. Operational definitions
Correlation and Regression20%- Define and use correlation
  • 1. Correlation coefficients
  • 2. Scatterplot interpretation
- Multiple linear regression
  • 1. Multiple predictor models
- Simple linear regression
  • 1. Model fitting and interpretation
- Logistic regression
  • 1. Classification modeling
Design of Experiments10%- Analyze experimental results
  • 1. Interpret experiment outcomes
- Define DOE and compare it to OFAT
  • 1. Experimental design fundamentals
- Best practices for experiments
  • 1. Randomization
  • 2. Blocking
  • 3. Replication
- Design a simple experiment
  • 1. Factor and response selection
Exploratory Data Analysis20%- Describe data using descriptive statistics and graphical summaries
  • 1. Histograms
  • 2. Pareto plots
  • 3. Probability distributions and normality
  • 4. Run charts
  • 5. Box plots
  • 6. Population vs sample
  • 7. Summary statistics
- Prepare data for analysis
  • 1. Derived variables and recoding
  • 2. Data quality assessment
  • 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
- Visualize and explore data
  • 1. Heat maps
  • 2. Trellis plots
  • 3. Scatterplots
  • 4. Bar charts
  • 5. Geographic maps
  • 6. Variability charts
  • 7. Data storytelling and communication
Decision Making with Data15%- Define and describe statistical intervals
  • 1. Confidence intervals
  • 2. Prediction intervals
- Perform statistical tests
  • 1. P-values and statistical significance
  • 2. Hypothesis testing
- Determine sample size
  • 1. Power analysis
  • 2. Sample size planning

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