SASInstitute A00-910 dumps - in .pdf

A00-910 pdf
  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving
  • Updated: Sep 12, 2026
  • Q & A: 0 Questions and Answers
  • PDF Price: $888.00
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SASInstitute A00-910 Exam Syllabus Topics:

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

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