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The SASInstitute A00-910 course SASInstitute A00-910: Statistical Thinking for Industrial Problem Solving provides a comprehensive understanding of statistical thinking and its application in problem-solving in the manufacturing sector. A00-910 course teaches the fundamental concepts of probability, data distribution, hypothesis testing, and regression analysis.

SASInstitute A00-910 exam is intended for individuals who have a solid understanding of basic statistical concepts and are looking to take their skills to the next level. A00-910 exam is ideal for professionals working in manufacturing, healthcare, finance, and other industries that rely heavily on data analysis. A00-910 exam is also suitable for students who are interested in pursuing a career in data analysis or related fields.

SASInstitute A00-910 Exam Syllabus Topics:

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

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