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SASInstitute A00-910 guide torrent - Statistical Thinking for Industrial Problem Solving

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A00-910 guide torrent
  • Exam Code: A00-910
  • Exam Name: Statistical Thinking for Industrial Problem Solving

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SASInstitute A00-910 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
- 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
- Process mapping and SIPOC
  • 1. Process maps
  • 2. SIPOC models
Topic 2: 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
Topic 3: Exploratory Data Analysis20%- Visualize and explore data
  • 1. Heat maps
  • 2. Geographic maps
  • 3. Scatterplots
  • 4. Trellis plots
  • 5. Data storytelling and communication
  • 6. Variability charts
  • 7. Bar charts
- Save and share results
  • 1. Create and customize graphics
  • 2. Reproducibility and work organization
- Describe data using descriptive statistics and graphical summaries
  • 1. Run charts
  • 2. Population vs sample
  • 3. Box plots
  • 4. Summary statistics
  • 5. Histograms
  • 6. Pareto plots
  • 7. Probability distributions and normality
- Prepare data for analysis
  • 1. Data quality assessment
  • 2. Handling missing data and outliers
  • 3. Grouping and binning data
  • 4. Derived variables and recoding
Topic 4: Decision Making with Data15%- Perform statistical tests
  • 1. Hypothesis testing
  • 2. P-values and statistical significance
- Define and describe statistical intervals
  • 1. Confidence intervals
  • 2. Prediction intervals
- Determine sample size
  • 1. Power analysis
  • 2. Sample size planning
Topic 5: Predictive Modeling and Text Mining5%- Predictive modeling concepts
  • 1. Model validation
  • 2. Explanatory vs predictive modeling
- Text mining fundamentals
  • 1. Extracting insights from text data
Topic 6: Design of Experiments10%- Best practices for experiments
  • 1. Randomization
  • 2. Replication
  • 3. Blocking
- Design a simple experiment
  • 1. Factor and response selection
- Analyze experimental results
  • 1. Interpret experiment outcomes
- Define DOE and compare it to OFAT
  • 1. Experimental design fundamentals
Topic 7: Quality Methods20%- Use Statistical Process Control Charts
  • 1. Process monitoring
  • 2. Control charts
- Assess process capability
  • 1. Capability analysis
  • 2. Variation assessment
- Conduct Measurement System Studies
  • 1. Measurement system analysis
  • 2. Gage studies

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