Statistical Thinking for Industrial Problem Solving: A00-910 Exam

"Statistical Thinking for Industrial Problem Solving", also known as A00-910 exam, is a SASInstitute Certification. With the complete collection of questions and answers, UpdateDumps has assembled to take you through 0 Q&As to your A00-910 Exam preparation. In the A00-910 exam resources, you will cover every field and category in SASInstitute Certification Certification helping to ready you for your successful SASInstitute Certification.

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SASInstitute A00-910 Exam Syllabus Topics:

SectionWeightObjectives
Quality Methods20%- Statistical process control and quality analysis
  • 1. Measurement System Analysis (MSA)
    • 2. Process capability analysis
      • 3. Control charts (SPC)
        Decision Making with Data15%- Statistical inference
        • 1. Hypothesis testing
          • 2. Confidence intervals
            • 3. Sample size determination
              Design of Experiments10%- Experimental design principles
              • 1. DOE vs OFAT
                • 2. Best practices in experimentation
                  • 3. Experiment design and analysis
                    Predictive Modeling and Text Mining5%- Predictive modeling fundamentals
                    • 1. Model validation and interpretation
                      • 2. Explanatory vs predictive modeling
                        Correlation and Regression20%- Relationship modeling
                        • 1. Simple and multiple linear regression
                          • 2. Correlation analysis
                            • 3. Logistic regression
                              Statistical Thinking and Problem Solving10%- Problem definition and process mapping
                              • 1. Root cause identification and data collection
                                • 2. Define statistical thinking
                                  • 3. Process mapping and SIPOC
                                    Exploratory Data Analysis20%- Descriptive and graphical analysis
                                    • 1. Data preparation and reporting
                                      • 2. Data visualization and exploration
                                        • 3. Descriptive statistics

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