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SASInstitute A00-910 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Exploratory Data Analysis	20%	- Visualize and explore data 1. Trellis plots 2. Data storytelling and communication 3. Geographic maps 4. Heat maps 5. Variability charts 6. Scatterplots 7. Bar charts - Describe data using descriptive statistics and graphical summaries 1. Population vs sample 2. Probability distributions and normality 3. Run charts 4. Histograms 5. Box plots 6. Summary statistics 7. Pareto plots - Save and share results 1. Create and customize graphics 2. Reproducibility and work organization - Prepare data for analysis 1. Grouping and binning data 2. Data quality assessment 3. Derived variables and recoding 4. Handling missing data and outliers
Topic 2: Correlation and Regression	20%	- Simple linear regression 1. Model fitting and interpretation - Multiple linear regression 1. Multiple predictor models - Logistic regression 1. Classification modeling - Define and use correlation 1. Scatterplot interpretation 2. Correlation coefficients
Topic 3: Predictive Modeling and Text Mining	5%	- Predictive modeling concepts 1. Explanatory vs predictive modeling 2. Model validation - Text mining fundamentals 1. Extracting insights from text data
Topic 4: Quality Methods	20%	- Assess process capability 1. Capability analysis 2. Variation assessment - Conduct Measurement System Studies 1. Measurement system analysis 2. Gage studies - Use Statistical Process Control Charts 1. Process monitoring 2. Control charts
Topic 5: Decision Making with Data	15%	- Perform statistical tests 1. Hypothesis testing 2. P-values and statistical significance - Determine sample size 1. Sample size planning 2. Power analysis - Define and describe statistical intervals 1. Prediction intervals 2. Confidence intervals
Topic 6: Statistical Thinking and Problem Solving	10%	- Process mapping and SIPOC 1. Process maps 2. SIPOC models - Root cause identification and data collection 1. Sampling strategies and data sources 2. Data collection planning 3. Operational definitions 4. Cause-and-effect diagrams 5. Brainstorming and affinity diagrams - 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
Topic 7: Design of Experiments	10%	- Analyze experimental results 1. Interpret experiment outcomes - Design a simple experiment 1. Factor and response selection - Define DOE and compare it to OFAT 1. Experimental design fundamentals - Best practices for experiments 1. Randomization 2. Replication 3. Blocking