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SASInstitute A00-910 Exam Syllabus Topics:

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